Using a regression in this research

writing case study and need a reference to help me learn.

My most important requirement is someone who is not busy and therefore can read a lot about my topic and spend time focusing on my work. I am always open to any suggestions or changes and would willingly make appropriate changes. The person who is willing to help me should have the following skills:
An excellent English speaker and writer also an expert on analysis design.
Someone familiar with the effects of YouTube search engine optimization on its content creators.
Someone who has had access to and the ability to find academic journals appropriate to the topic
A person who is very good at writing strong and knowledge-based papers of appropriate length.
I am looking for someone with critical and analytical thinking who also has good explanation skills
Great writing skills and the ability to use various resources
Used and familiar with APA citation format 7th edition. Must use APA citation format 7th.
Note:
I use two plagiarism detection tools on the Internet, safe-assign, and Turnitin. So if I see any content without citation and reference, I will ask for cancellation immediately, and there is no chance to redo it. Of course, I will provide a report for that to show you where you plagiarised. Sorry for the long talk, but I want you to know that I am looking for free plagiarism work and high quality.
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I estimated that it would not be less than approximately 2-3 pages to edit the issues.
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I wanted to share this dissertation with you as it will give you the wording and the understanding of my own work.
I believe you are better off approaching my regression in the same manner as Kenneth did in his dissertation.
So please go to Chapters 3 – 4 and see how he set up the regression for his multiple independent variables.
For example in my research, on p 65, I still have the reference to a regression that I did not do and is inconsistent with the procedures I ran. Can you be sure to go through the document once more and ensure all the comments have been addressed?
– Even better, you should include references to the publications and presentations in chapters 3, 4, and 5.
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The effects of YouTube search engine optimization on its content creators
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Abstract
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Search Engine Optimization is an ever-changing set of practices for designing and determining which web content will rank high on search engine results pages (SERPs). In addition, search engines also model how YouTube uses algorithms to yield search results. Once a web page is optimized for YouTube, it will be optimized for different search engines. This researcher will explain how content creators can make their YouTube channel appear on YouTube’s search engine to gain the highest view through their channel’s videos by growing PageRank to improve visibility, outreach, and profit. Through exposure theory, this quantitative study explores how to achieve search engine optimization (SEO) for running a YouTube channel and the optimization effects on content creators. Factors affecting the optimization procedures to get ranked in the pinnacle search outcomes include number of views, subscribers, comments, likes, and other interactions. The effects of content creators’ videos across search engines will be discussed through audience attraction factors in various ways, such as trending tags, Thumbnails, keywords, etc. Therefore, YouTube acts as a search engine. Strategies for achieving SEO include linking content creator to web optimization for their channels to expand productivity in subscribers, views, and interplay among various factors.
Keywords: YouTube, Subscribe, Ranking, Thumbnail, Optimization, SEO.

Dedication
Acknowledgments

Table of Contents
Page

Table of Figures

Table of Tables
1. Introduction
Overview
YouTube is one the most prominent internet platforms with billions of subscribers and viewers (Nyagadza, 2020). According to Giolemakis et al. (2015) and Pedotto (2018), the creation of the World Wide Web in 1989 to Web 2.0, in comparison with current Internet technologies and the growth of advanced search engines over the last ten years (Berners-Lee, 2009). It has come with significant adjustments in Web information, such as user-generated content, metadata, or social web (Berners-Lee, 2009). Therefore, internet platforms like YouTube developed better viewer experiences using search engine optimization (SEO) tools to improve user time spent on searching and viewing content. Content creators are tasked with creating user-friendly channels and enhancing the visibility and accessibility of the content shared on YouTube platforms. Because YouTube is a large platform, ranked second only after Google, it offers one of the most significant search engines globally (Nyagadza, 2020). Thus, YouTube has created a large platform that has a monopoly on user searches for videos. For instance, eight out of ten videos googled will generate a YouTube video result (Giomelakis and Veglis, 2015).
Advertisers and video content creators have developed a habit of marketing and promoting their products on YouTube channels to capture more viewers as customers for their products (Almurashi, 2016). Moreover, for the company to stay in the top-ranking place regarding video searches, it has utilized various SEO tools that enable creators to manage and increase their viewers. SEO tools used within the YouTube platform include Ahrefs Keyword Explorer, Canva, Hubspot, VidIQ, Vision, TubeBuddy, and Cyfe (Cowley, 2020). This research will analyze how content creators use YouTube channels with the help of SEO tools to boost the ranking of their videos to the top and capture more viewers with keywords.
Different digital platforms have emerged, making information more available to the public and the YouTube website (Sharma et al., 2019). Increased online viewers and readers have made internet platforms the go-to option for many people globally. For content creators willing to market their products and services online, higher traffic has made the internet a central competitive environment. Due to competition, various creators and organizations have adopted innovative approaches, such as using SEO tools, to help make their content easily visible and to help it stand out from the rest (Swapna and Anuradha, 2018). As a result, introducing an SEO approach to content shared on YouTube channels has potential benefits as it helps increase web traffic, creates a better visitor experience, helps videos become more visible, and enhances authority.
Statement Of Problem
Most people use YouTube to create different videos to share with friends, family, education colleagues, and others. Also, some YouTubers create content for the public to spread knowledge, according to their field of interest, and the way the content is presented can be educational, entertaining, scientific, etc. (Saurabh and Gautam, 2019). Some find it challenging to spread the content to novice YouTubers. people sometimes notice channels with different content but little demand from subscribers, views, or likes. Furthermore, that little turnout is often due to a weakness in some essential points to activate the process of spreading videos, such as video hooks, hashtags, thumbnails, and others (Richardson et al., 2007).
Many YouTubers miss some of the essential YouTube search engine optimization methods (Jackson and Baum, 2011). These SEO tools are simply the beating heart and driving wheel of most YouTube videos. YouTube usually supports some emerging channels by pushing their videos to some viewers (Krasanakis et al., 2020). Alternatively, the video topic may be a trend on social media, so it goes viral (Xing and Lin, 2006). Many YouTubers get frustrated at first with not receiving enough views or likes that many of them upload content with the video title and simple words in the video description. Therefore, a video is isolated from other videos and does not reach a large segment of people in the surrounding or global communities.
During the global spread of the Coronavirus, we noticed that some teachers resorted to social media platforms to provide explanations for the curriculum, including the YouTube platform. Consequently, teachers should use videos as a means of not only educating their own students but also providing information to communities beyond their own classrooms (Bennett, 2022). As a result, they made it easier for modern and future college students to access them easily. Likewise, some teachers were not satisfied with that but instead began to think about spreading educational content to a segment outside the educational institution. Teachers and coaches can also make money through social media and monthly salaries. Nevertheless, some do not know enough to deliver their content on the platform perfectly. Therefore, this study presents strategies and techniques to improve video access distinctly and smoothly.
Purpose of the Study
This research aims to improve YouTubers’ channels by applying numerous optimization processes to achieve a high-ranking in-search engine result. The author tested exposure theory by modifying models tying YouTubers to SEO for their channels to improve subscribers, views, and the interaction between multiple aspects.
Significance of The Study
The following are the project’s main intentions:
• To place a specific YouTube page in the top search engine.
• To design a user-friendly website with a wide range of content.
• Enhance my targeted audience’s experience by upgrading my site graphics.
• Reduce the amount of competition on the present website by removing it.
• A basic understanding of how search engines work and their strengths and weaknesses.
People worldwide have relied on YouTube for years as their go-to search engine because it consistently delivers their desired results. By improving the PageRank of the YouTubers page, YouTubers can make it one of the best-ranked websites on the search engine, increasing their company’s visibility and profit (Duffy, 2008). Another reason we focus on it is that other search engines also employ the YouTube layout. As a result, once a web page has been optimized for YouTube, it will also be optimized for different search engines, including Google (Jaffari, 2019). 
Research Questions
What do YouTubers do to improve audience retention? 
What tools do YouTubers find most useful?
Validity and Reliability of the Research
Validity and reliability are significant in every research. This study utilizes Creswell’s (2014) definition: validity checks for the findings’ accuracy, while reliability shows consistencies across various projects and researchers. This research uses quantitative research in the analysis for triangulation purposes to achieve this research’s validity and credibility. The sources this study has consulted converge, leading to the research’s validity. This research is critical to content creators and youtubers, and through this synthesis of existing materials, this research aims to provide valid and reliable information necessary to search engine optimization.
Definition of Terms
For a better understanding, the researcher operationally defined the following term/s:
Search Engine Optimization
SEO is optimizing your content to receive organic or unpaid for traffic from the search engine results page. Keep SEO principles in mind when using YouTube, as it is becoming increasingly crucial to attract traffic, create a following on the platform, and optimize your videos (Xing & Lin, 2008).
Click-Through Rate
The number of clicks YouTubers’ ad receives is divided by the number of times their ad is displayed (Richardson et al., 2007). Use the number of clicks to know whether their ad appeals to those who see it. When you click on a video, you will see how many times it has been viewed.
View-Through Rate
In other words, it is the number of completed views compared to the number of initial impressions for skippable ads (VTR). As a result, VTR = Complete views (where the user did not skip)/impressions (advertisements rendered) (Pedotto, 2018).
YouTube Video Tags
YouTubers may add tags—descriptive keywords—to their video to make it easier for visitors to find their material (Ramdani, 2021). The title, thumbnail, and description are crucial information elements for their video’s discovery. These critical pieces of information aid viewers in selecting which videos to watch.
YouTube Video Thumbnail
YouTube thumbnails are the small preview images used to represent videos (wdc_dev, 2022). Their job is to grab people’s attention and convince them to watch the video. YouTube thumbnails are like book covers. They are thumbnail representations of video content designed to stimulate interest and curiosity.
YouTube Shorts
Using only a smartphone and the Shorts Camera in the YouTube app, YouTube Shorts are a means for producers to reach out to new audiences (Admin & Author, 2021). And using your Multicam, making short videos up to 60 seconds is simple, thanks to YouTube’s Shorts producers.
PageRank (PR) Algorithm
An approach to gauge the significance of internet pages is through PageRank. Google Search uses the PageRank (PR) algorithm to rank websites in their search engine results (Musib, 2022). Google developed PageRank after one of its co-founders, Larry Page. Google claims that PageRank determines a page’s significance by tallying the number and caliber of links (Krasanakis et al., 2020). The essential premise is that more significant websites will probably get more links from less critical websites.
Literature Review
Introduction
This chapter describes how PageRank improves YouTubers’ channels, enhancing visibility and channel reach by making it one of the best websites listed for search engine visibility. Since it provides the user with the desired results, YouTube has become the most used search engine for customers worldwide (Cho et al., 2020). Moreover, other search engines use the YouTube approach, so we focus on it. Thus, once the webpage is optimized for YouTube, it will be optimized for different search engines (Berman and Katona, 2013). This chapter contains the findings of the literature review and is organized as follows:
Enhancing the use of YouTube search engine optimization
Search engine optimization tools that are Efficient for a YouTube page
Additional Search engine optimization features for YouTube page
Search engine optimization techniques for improving YouTube page
The Effective Use of YouTube Videos for Teaching.
Enhancing YouTube Search Engine Optimization
Identifying Optimization Strategies
At a video level, the correlation between the most viewed videos of each cybermedia reveals the presence of two families of video-level (Lopezosa et al., 2019). The metadata sections that provide information about the video title, description, and duration, and the thumbnail and the name of the uploaded video file are the most important on YouTube. YouTubers have access to a different placement algorithm used by Google, which allows the platform to arrange the videos displayed in search results. Interactivity is more psychological and, importantly, includes the call to action. It entails compelling advertising of the video so that viewers will subscribe, comment, favorite, like, or detest it and watch more relevant videos, among other things. YouTube video optimization best practices. Our findings show that the most popular videos attained their status despite the employment of active SEO tactics. Implying that the quality of the publication, the subject discussed, or the characters included in the video were the factors that contributed to their success.
The Role of Search Engine Optimization
The algorithms used by search engines assume that they fired their SEO contractor after finding a measurement error (Berman and Katona, 2013). That prevented them from using black hat techniques that eventually led search engines from perfectly ordering links according to a punitive response from Google. They model SEO as a static force that will initially make the search engine’s revenue game. Berman and Katona’s paper show that SEO is If when advertisers’ Val- She questions for organic links are high enough. Considering sustainability, SEO service providers can then free organic rides on the search engine due to their parasitic nature. The relationship between advertiser qualities and valuations and the strategic nature of consumer link search is not considered. Search engine scores each website searched on through its all-estimated links. It has decided to stop searching and consuming, and payoffs are realized.
Search Engine Optimization Strategies
YouTube and other major social networking sites rely on algorithms to decide which content gets shown to more people (Khan et al., 2018). These online companies consistently and heavily weigh one factor: PageRank (PR). PR was born out of the PageRank algorithm created by Google and is commonly used to rank web pages on their search engine. Various factors determine how prominently a page or item will appear in search results or on a user’s newsfeed, with an algorithm worth using. Various search engine optimization strategies and tools are efficient for ensuring effective participation in YouTube. Due to the amount of content uploaded daily, a YouTube page needs to be customized with various SEO tools and strategies to ensure that it is effectively being used as an interactive medium and increasing the participation of others (Khan et al. 2018). These strategies usually enable the website to get more viewers and make more profits. Also, by improving the visibility of its content, the website will prevent websites from duplicating content.
Search Engine Ranking Algorithms
One way of ensuring more participation of people on YouTube is by increasing the PageRank (Hingoro et al., 2021). One way of achieving this is by picking a particular keyword after the content’s keyword, creating a video, and adding a title that includes the specific word or phrase needed to be ranked. This should be used in a natural way, not forced. Then, YouTubers should copy and paste it again somewhere within their description. For instance, educational content will be easily seen by including the topic’s keywords, making it easily visible in the description. Also, it is essential to have a hashtag relevant to the subject of the video. These keywords will help more people find their videos through the search. One solution would be to change how these two text fields are displayed on YouTube. Hence, to make content easily accessible, YouTubers should show the video title and description on the video search results page (Hingoro et al., 2021). The new method would be a small amount of text at the bottom of these two fields so that when people come across their video, they can skim through all the info about their video without reading through and watching all 14 lines of information on their channel.
SEO Techniques to Achieve High Page Rank
Search engine optimization techniques use captions and subtitles (Kaur, 2018). The two usually enable the accessibility of a video in numerous ways. YouTubers are a great way to communicate with their audience. Captions are often used because the deaf and hearing impaired cannot always hear the audio in other languages. Subtitles are used for different languages for those with visual impairments or a language that is not the first language they speak. The subtitles allow someone to get more out of what they are watching without needing an audio translation which reduces costs dramatically and is exclusive to certain scenes or segments of the video. Moreover, subtitles and captions allow the video to be indexed across multiple search engines. This situation means that the text within the subtitles is also searchable and gives their video a better chance of being seen by more people. Having a quality Subtitle or caption script can be challenging. Before scripting, one should remember that YouTubers should be appropriate for all users, so they must be easy to read and understand (Kaur, 2018). The second item you must consider is that the timing of your subtitles should be correct. Otherwise, it will not work with other scripts. Subtitles benefit educators and students who prefer using YouTube for learning. It enables students to have a good memory of the video they watched, improving comprehension and attention.
Optimizing Videos
Proper optimization is one of the critical things in attracting a larger audience on a YouTube page (Tafesse, 2020). The first thing one should do here is focus on the video’s title, description, and tags. These are the most likely ways people find it in YouTube search results or recommendations when using their social media profiles. One can also focus on categorizing the video and getting it listed in relevant categories. YouTubers also achieve transparent optimization by creating an entertaining video that many people will be interested in watching. A good title is also essential in optimizing videos because it helps people find videos using search engines like Google and YouTube (Tafesse, 2020). The tags should be catchy, original, and unique, but not overused. The description of a video is what the viewer will read before deciding whether to watch it or not. Thus, one should always make it sound exciting and appealing to the viewers. With a good description, the video will attract more viewers, increasing the participation of viewers on YouTube. Additionally, proper educational descriptions usually give students a clear image of the content they are about to watch, making the YouTube page a better interactive medium.
Search Engine Optimization Tools That Are Efficient for YouTube
Using Keyword and Feature Analysis
Though strong content is essential for attracting traffic to your video, if the goal is to maximize the number of views, the content must be helpful to the viewer, informative, shareable, and powerful (Choudhari and Bhalla, 2015). Many commercial organizations are establishing channels on video search websites where they can broadcast videos and make money from the traffic they generate. It is also simple for them to link it to their main website and other social media. Several different strategies can help the video rank higher. The first being the extraction of keywords which involves image processing techniques to summarize the video content and other media content features like thumbnail, subtitle generation, and annotation, helps the learner learn about the video. Secondly, video tags are also used to increase ranking. While uploading any video to any video search engine, one should keep that video name relevant to the keyword. Video attributes, like share and comments, help the search engine to gain trust for the video. Finally, when considering the Conclusion & Future work in this study, the author has analyzed the video tube features for exploring the area of VSEO. The focus was on video search optimization; the methodology identifies critical attributes for a video while searching. Showing video search engine as keyword selection strategy, which is a deciding factor for ranking of the video.
YouTube SEO: Everything You Need to Know to Rank
The type of video you utilize is crucial because if your films do not match the overall search intent of viewers, you will have less engagement, even if you initially rank high (Engaio Digital, 2022). Using significant and relevant keywords that fit your video’s topics will help YouTube categorize and recommend your video to viewers interested in such issues. A YouTube video’s thumbnail is your chance to persuade visitors to watch your video in any place where YouTube can display it. Because YouTube crawls through subtitles, customizing them to match your video will help it rank higher. While you can expand your films by 10% without losing quality, going above and beyond to lengthen them will degrade video quality, lessen engagement in your channel’s videos, and eventually affect your rankings. A relevant video on your blog article regarding the same topic can increase the video’s views and engagement on your blog post, which can help your site rank higher. YouTube provides a variety of perspectives on collected data, allowing you to expand your channel by progressively improving everything video by video.
YouTube SEO Tools to Boost Your Video Rankings
YouTube is one of the most significant search engines globally, with over 2 billion monthly active users (Barysevich, 2022). Most people consider YouTube a social media platform. YouTubers should improve their video content so that it is critical to rank in search so that audiences can find you on YouTube. Many typical YouTube website positioning regulations apply; discover the proper keywords to communicate relevance, diagram an exact user ride to power engagement, and use analytics to locate and restore whatever is not working. Although quietly is not explicitly created for YouTube, it is easy to integrate. Carefully selected, relevant content is considered for real-world research, bringing keyword ranking opportunities. It is important to find your competitors. Many tools can give you a rich insight into your competitors’ presence on YouTube. Social insider’s YouTube Competitor Analysis Tool provides an in-depth look at your ranking history, performance, and competitor’s content.
YouTube Video Tags
YouTubers want to select tags that communicate to YouTube and their audience the categories where YouTubers will reasonably include their video (BrightEdge, 2022). YouTube video tags can be valuable tools in establishing the topic and relevance of their video. It contains tags detailing the specific cases their video covers, such as keywords about the names of famous or popular people/brands in the video, locations, or other identifying features. YouTubers should include tags of common misspellings or other regular mistakes to help capture the portion of their audience making these errors. YouTubers should select their tags wisely to correctly classify their content so that the right audience will find it. YouTube allows YouTubers to use as many tags as they want if the total character limit across all tags is less than 500 characters. They ensure that their tags do not mislead users; having inaccurate tags can increase their bounce rate and thus hurt their rankings.
YouTube’s Advanced Video Manager (AVM)
YouTube’s Advanced Video Manager (AVM) is a robust search engine optimization (SEO) tool that can be used to improve the visibility and ranking of a YouTube video channel on Google’s search engine results pages (SERP). This strategy is designed to help a video channel rank higher in YouTube’s search results by providing specific metadata tags recognized as critical phrases by Google’s ranking algorithms. AVM benefits commercial YouTubers, teachers, and educators who use YouTube to deliver educational content to their students. By utilizing AVM, teachers can ensure that their educational videos are easily discoverable by their students, making them a valuable tool for promoting learning (Maya, 2019). With AVM, teachers can update their educational content, which will always be first on the SERP. Students can easily view the videos, particularly for remote learning or flipped classrooms. AVM can also be useful for individuals trying to help other teachers by providing educational content. These individuals can use AVM to promote their content, making it easily discoverable by other teachers looking for educational resources. This can be particularly beneficial for educators in under-resourced schools or communities who might need access to the same resources as others. Furthermore, AVM can also be used by YouTubers to help them reach a larger audience for their educational content. This can be particularly beneficial for YouTubers who are focused on informal education. Informal education refers to learning outside the formal school setting (Maya, 2019).
Additional Search Engine Optimization Features for YouTube
The Impact of YouTube Recommendation System on Video Views
YouTube, which has millions of videos, has several features to assist users in finding the content they are interested in. It provides video searches, related video recommendations, and front-page highlights (Zhou et al., 2010). Understanding how these features drive video views helps create a strategy to drive video popularity. Even though the YouTube video search is the number one source of opinions in aggregation, the related video recommendation is the primary source of views for most videos on YouTube. Their results reveal a strong correlation between a video’s view count and the average view count of its top referrer videos. They also discovered that a video’s click-through rate to related videos is high. The position of a video in a relevant video list significantly impacts the click-through rate. Finally, their analysis of the influence of the linked video recommendation system on video view diversity suggests that the existing recommendation system aids in increasing video view variety in aggregate.
Benefits of Using YouTube SEO Services for YouTuber’s Business
When YouTubers use YouTube video SEO services to help them get the most out of their content, they will put themself in a position to yield maximum results for their business (Dugan, 2020). Zero Gravity Marketing, a YouTube SEO business, can attest that this is a social media channel that should not be disregarded when creating campaigns. YouTube features a well-developed advertising framework, which Google Ads manages. YouTube videos should not be limited to one platform. YouTubers can share them on other social media platforms, embed them on their websites, and use them to bolster their blogs. In addition to the advantages listed above, a YouTube marketing agency may assist YouTubers in achieving additional branding. YouTubers should make a video marketing plan for their channels. It is critical to design a strategy before you start optimizing your YouTube channel. Like any marketing campaign, their YouTube efforts should include Natural SEO and other elements their agency can help YouTubers secure.
Correlating Prosodic Variation with Counts of Subscribers
Based on the following hypotheses, the study investigates whether gender impacts the speakers’ origin (Berger et al., 2019). H2: North American speakers have higher subscribers, views, and likes than British English speakers. H3: Male speakers have higher subscribers, ideas, and likes than female speakers. The data for this study consists of around ten minutes of speech material gathered from 10 YouTube Creators’ videos, or about one minute per speaker. The speakers were between 24 and 32 years of age at video publication. All videos have in common that the speakers tell stories meant to entertain, inspire, and open a discussion about the topic with the viewers. The models predicted the dependent variables: the highest subscriber count of a speaker, the subscriber count of the channel the video was from, the number of views of the video, and the number of likes. Counts of subscribers, preferences, and views were higher for male speakers than for female speakers, and subscriber counts were higher for North American channels than British channels. Future studies should also analyze more speakers and compare speakers of different success levels on YouTube.
Introduction of A Teacher Mode Feature
Introducing a teacher mode feature in the search engine optimization software can also enable a higher PageRank (Krasanakis et al., 2020). This would make it easier for students to learn on YouTube and apply what they have learned in their classes. It would also make it easier for teachers to find the specific videos they want to use during their course. The teacher mode would be a great learning tool to improve how students can use YouTube to learn. Moreover, this feature would create a culture where the students learn from YouTube and then take what they have learned to apply it in their classes. The teacher mode would be a great addition to YouTube so students can use it to its fullest potential. Moreover, this feature would improve teachers’ teaching abilities by quickly searching for the educational videos they want to use and finding the specific videos they need. Hence, this would make it a more interactive medium for teachers and students.
Using Machine Learning for Web Page Classification in SEO
One way to improve YouTube web pages is by having better search engines. Better search engines usually enable students to find the right channel of educational videos that they want and then watch them. These are the search engines we use in this class to find educational videos on YouTube. These search engines would help people find the right video and how fast those searches could happen. Search engines could make it easier for people to watch educational videos by improving it in different ways such as user interface, reduction of time taken, and making it up to date with the new technology (Matošević, 2018). The user interface can be enhanced by using a new and better design. With the new design, it will be possible for Google to show what is available to students for educational videos. A better interface will help students get more information about the videos and find them attractive. It would also enable search engines to develop new features without issues. This would be directly beneficial to me as a YouTuber since I could easily customize the settings of my webpage and enable my content to be easily accessible by the students.
2.4.6 The Significance of Search Engine Optimization
Implementing an SEO strategy offers various significance to marketers and promoters selling and promoting their products online (See Figure 1). The ordinary significance of SEO tools used on YouTube includes off-page promotion and SEO reporting, whereby content creators can promote their videos using an off-page link that directs viewers to their YouTube channels (Kakkar et al., 2015). Therefore, using SEO can optimize a YouTube page using the main keywords of the video title, thus allowing the creators or promoters to develop content optimization and build backlinks, which is commonly used in Google. SEO can also be used to analyze the amount of traffic a video receives and analyze the channel’s ranking for users to have easy accessibility. According to Kakkar et al. (2015), the dynamic process of SEO makes it a long-term focus aspect. The algorithms used by Google and the SEO strategies must develop variations and require constant monitoring and adjustments. Moreover, SEO enables organic results – non-paid advertisements and searches resulting from an individual’s search history and preferences as profiled by the system. Gudivada et al., (2015) find that more than 70% of internet users prefer organic searches. Therefore, using SEO on YouTube is a critical competitive advantage for the organization and allows content creators and channels to have better chances of long-term engagement with users.
Figure 1
Image showing the significance of SEO
Search Engine Optimization Techniques for Improving YouTube
Employing Search Engine Optimization Techniques
Search Engine Optimization pertains to the practices aimed at increasing the visibility and traffic a Web site or a Web page receives from organic search engine results (Giomelakis and Veglis, 2015). Using main, appropriate words/phrases that people often use in their Web searches and putting the most search-friendly words ahead for search engines to «read» them is crucial to SEO. Frequent usage of proper names and locations is also recommended. The sooner a keyword appears in the text content, the more likely it is to be relevant in a search for that keyword. Adding suitable video content apart from the main text adds value, makes content even more prosperous, and is often ranked better/higher by search engines. The search engine system examines how the article’s text is formatted using these tags to determine the essential words on the page. Search engines prefer websites with consistent and easy-to-read URLs. Social networks increasingly affect search engines’ algorithms, and many experts consider them a considerable part of SEO.
Exploring Content Trends Across the Most Subscribed YouTube Channels
Several quantitative content analyses have attempted to grapple with YouTube celebrities’ perceived authenticity and trustworthiness, intimacy between YouTube celebrities and their audiences, and YouTube content creators’ sense of celebrity identities. Recently, research on YouTube creators has focused on how these relatively amateur performers become celebrities through user-generated content on YouTube (Ferchaud et al., 2018). User-generated content constitutes more than two-thirds of YouTube’s most discussed and most responded to content. This study has provided demographic data and genre classifications for individual viral videos, revealing that violent content is more prevalent on television than on YouTube. They were given popular media’s glorification of YouTube celebrities as a new entrepreneurial dream and Burgess and Green’s suggest that YouTube celebrities influence the broader cultural norms and practices of YouTube. This study’s findings may begin to suggest the direction of more general trends in content presentation for web videos.
Product Placement on YouTube
Video creators and their YouTube channels have become the corresponding author, increasingly Claudia Gerhards since Google’s acquisition of YouTube in 2006 and the increasing professionalization of the sharing site’s content (Gerhards, 2017). Although YouTube channels as an advertising space have been receiving considerable attention from marketing departments and media agencies, virtually no research findings are available about YouTube creators’ experiences with product placement inquiries and the relations and processes between creators and advertisers. Increasingly, studies consider that YouTube and its video creators follow a strategy of professionalization. It makes the video-sharing site and its content more compatible with the interests of advertisers. Fearing the entrepreneurial desire among amateur creators and YouTube’s methods of harnessing user-led cultural production into profit generation, some critics assume that some users could damage the participatory culture of YouTube.
Modelling and Statistical Analysis of YouTube’s Educational Videos
This search engine optimization strategy is highly recommended for people who want to make educational videos using various software applications. This practice is highly recommended for teachers and instructors who want to make high-quality educational videos for their channels (Saurabh, 2022). A script can be used for educational videos to keep track of your information. Moreover, it also helps you establish and achieve the goals you have aimed for in your content production process. A properly planned script can help one produce educational videos that can easily be found on the internet, specifically YouTube. When researching online, students can find the best educational videos available on YouTube. Scripting high-quality educational videos is an important way of attracting people to be content creators. High-quality scripted videos, especially those scripted by professionals, have the potential to attract more viewers and increase the retention rate of content created. This search engine optimization strategy usually enables content creators to get more viewers for their videos and increase their engagement with them. Moreover, it can also improve the ranking of a video on YouTube. Quality scripting also needs to involve some key features. It should be well-designed, helpful, informative, and engaging to viewers.
Using Hybrid Modified Multiple Criteria Decision Making (MCDM) Models
On-Site optimization can be defined as optimizing the site on the underlying meaning of SEO. There are many ways to optimize a site, but one way is to optimize videos on YouTube with keywords in a specific manner. This typically enables an increase in the views, clicks, and, ultimately, the time a video is viewed. Optimizing YouTube videos relies on keywords and tags written explicitly in the video. Then, the videos are submitted for approval. The optimization process is done by submitting the video to many other websites (Tzeng, 2021). Hence, it effectively ensures that educational content for students on YouTube is presented to more learners. This also enables teachers to find their resources and use them to create lesson plans which allow exclusive learning for students. On-Site optimization also aids in increasing participation on YouTube because it usually attracts more attention from people searching for your specific video. This motivates people to start creating content and uploading it on YouTube since the promise of getting a video to the top is easier and faster. This increases the demand for more YouTube videos. External linking can be defined as connecting YouTube videos to external sites. External linking is, in effect, a search engine optimization strategy that increases the chances of acquiring a high ranking on YouTube’s SERPs (search engine results pages).
Increasing Traffic for a Particular Video
Increasing traffic to a YouTube channel is an effective way of increasing the participation of others on YouTube (Vaish, 2020). The more views a YouTube channel has, the more likely it is for individuals to contribute. This is because it enables individuals to watch more videos to achieve some of their own goals. Moreover, it also helps create a sense of self-efficacy in YouTube viewers. They learn about the feats individuals can accomplish when they follow others with similar goals (Vaish, 2020). Search engines, such as Google or Bing, use high-quality videos, rich media details, and relevant keywords to determine which videos are ranked higher and placed at a lower position for the same keyword. This suggests that YouTube is a good way of promoting participation in different countries because more views on a channel means more people are likely to view the video and participate in it.
Social Media Networking
The search engines usually identify videos with many backlinks to be highly relevant to their targeted keyword (Jackson, 2022). External linking can prove to be very effective in this situation. The more external backlinks the video has, the higher it ranks on the search results pages. YouTube also provides a link error message to those who use links other than those provided on its site. This is due to its effort to prevent users from abusing external links in their video descriptions. The numerical ranking of a video can also be increased by external linking. A website with a high-ranking page will usually get many backlinks from other websites. For example, if the video has a rating of 4.5, the video will likely receive hundreds of backlinks daily. Some online tools can be used to check external links in YouTube videos (Jackson, 2022). One such tool is Link YouTube Page which helps users monitor videos for external relations and create an index of these links. These practical tools also promote the participation of other individuals in watching the videos and serve as motivation for content creation.
Achieving Higher Ranking to Webpages Through SEO
Link building can be defined as creating, adding, or obtaining links to a website by any means, hoping these backlinks will improve the website’s ranking in search engines like Google and YouTube (Swapna, 2018). It is an effective search engine optimization strategy that can gain visibility, generate traffic, and increase your website’s ranking in search engines. Link building as a search engine optimization strategy has been highly influential on YouTube because it provides more opportunities to improve search engine rankings. A YouTube page can use the link-building strategy to ensure that it is promoting its content effectively. One of the main strategies of doing this is using it in conjunction with the video. This can be achieved by including links in the description of the video. It is important to note that YouTubers are advised not to include links that do not belong to them in their description box since it can result in disagreements and lead to trouble. It is also essential that the links are relevant to the specific video (Swapna, 2018). It is also necessary to gain exposure from people who visit the YouTube channel and share the video on social media platforms like Facebook and Twitter.
The Effective Use of YouTube Videos for Teaching
Using New Media
John Seely Brown used ecology as a metaphor to describe a learning environment john Seely Brown describes a learning environment using the metaphor of ecology in his article Growing Up Digital: How the Web Changes Work, Education, and the Ways People Learn. in his article Growing Up Digital: How the Web Changes Work, Education, and the Ways People Learn (Duffy, 2008). This study looks at blogs, YouTube, and wikis as illustrative and famous examples of technologies and websites that illustrate the shifting terrain of our Web 2.0 learning ecosystem. Before exploring some viable techniques for educators to include blogs, YouTube, and wikis in the student learning experience, definitional components of Web 2.0 and a broad grasp of Web 2.0 are explored. When combined with hands-on learning, new media and a video-enhanced curriculum may help widen the learning experience. Educators can tap into the existing enthusiasm for this sort of new media by introducing a medium as popular, forceful, and recognizable. Jenkins describes the ‘university’ in higher education as an intellectual network in which students interact not only with professors but also with industry and the community, implying a shift in the traditional classroom learning ecology and the inclusion of collaborative broader perspectives usually described as a blending of online and face-to-face learning experiences.
How Content Creators Succeed as an Academic on YouTube
Informal learning blurs the barriers between how content is characterized by its very nature (Maynard, 2021). Regardless of how content creators or researchers categorize videos, many YouTube users are likely learning from many different types, styles, and genres of video. As more academics develop legitimate YouTube material, viewers will access a broader content of techniques and methods to learn and profit from. Academics have a largely untapped opportunity to add to the richness, diversity, and reliability of video content available to casual learners and effectively deploy their knowledge. These results show that viewers strongly desire educational information and that educational channels on YouTube can potentially reach many casual learners. Despite this professional education channels’ availability, YouTube is saturated with substandard and misleading information that informal learners are forced to navigate when conducting searches. Choosing a topic that intrigues and has relevance to your audience is essential because people leave films that do not offer pertinent information (Maynard, 2021). We can see from the graph (See Figure 2) that the main reason viewers stopped watching was that they were not provided with the information they were expecting. There are also gaps in YouTube content when renowned educational creators do not have the time or the incentive to produce the diversity of material that casual learners seek.
Figure 2
Graph displays the main objections to watching training videos.
Scripting High-Quality Educational Videos
Scripting high-quality educational content videos is another factor that can make a YouTube channel a tremendous interactive medium between students and teachers (Cho, 2020). This search engine optimization strategy builds a more substantial relationship between teachers and students. This SEO strategy builds a stronger relationship between teachers and students. Moreover, it strengthens the relationship between students and their study content. Hence, students can see the desired content before watching it in a video. Therefore, teachers using scripts of educational videos can present their lessons more interactively. As a result, students can have an even more precise idea of what they will learn. There are many advantages of using the right content in educational videos, regardless of length. Depending on the topic being taught, a script can be used to keep a clear and consistent message (Cho, 2020). This method is more effective than face-to-face communication when conveying several ideas or concepts. For example, by using YouTube as a platform for creating video lessons, teachers can take advantage of it by scripting most of their videos. Hence, it is one effective way of ensuring that a YouTube page has increased its PageRank and is helpful to students and teachers.
Using YouTube Videos for Teaching Language Skills
YouTube has become one of the most famous websites globally (Almurashi, 2016). The paper discusses the influential role of multimedia text in many YouTube videos for teaching English in the classroom. Future research into the effective use of YouTube videos in English language teaching and learning is essential. Specifically, we must investigate learners’ attitudes toward using YouTube, the negative fears that learners may have while learning new languages ​​using YouTube, and teachers’ experiences with using YouTube videos. YouTube multimedia text can play a leading role in helping learners understand English lessons. Students agree that YouTube English lessons can help them understand the practical and exciting advantages of watching YouTube lessons. One learner said that Every day, s/he watches videos on YouTube, like movies, news, and studies too, and another learner supported a similar point when she answered: Sure, s/he knows YouTube very well, and s/he has their account. As a result, using YouTube is not complicated if teachers upload lessons or ask their students to watch a video for learning purposes.
Faculty Usage of YouTube as a Health Education Resource
YouTube Overview, created in 2005, YouTube provides a publicly accessible web-based platform that allows people to upload, view quickly, and share videos on the World Wide Web. This research aimed to decide on faculty members’ cutting-edge and conceivable use of YouTube in their classrooms (Burke et al., 2009), which identifies manageable limitations and challenges of this online resource. They also identified faculty perceptions of the benefits of YouTube as a health education resource for in-class and online courses. 7 YouTube apps in higher education based on lookup from the Pew Internet and American Life Project report that most university students already use YouTube-like applied sciences in their private lives. Seeing this kind of platform in the classroom will probably be familiar as an educational supplement.
Students Are Comfortable Using Tech with Their Studies
Teachers can use YouTube to support those students because of their approach to digital learning. They are more accustomed to using technology such as the Internet, vlogging, and texting than traditional classroom learning tools (Burke et al., 2009). Other faculty perceptions of using YouTube in the lecture room are seen in their open comments, where several surveyed individuals shared their perceptions of using it in their courses. The most frequent reasons respondents reported for non-use have been unfamiliarity with YouTube and using different video assets instead (Sharma et al., 2020). One respondent commented that s/he still needed to list it S/he has other tools; however, they must see what YouTube can offer.
The Usage of social media for Formal and Informal Learning
Over the years, many people have been using social media. Nowadays, people search for information on social media platforms (Czerkawski, 2016). Formal learning involves learning in educational institutions. In this learning process, students adopt informal learning, which can happen at any location, such as museums. Social media is known to connect both formal and informal learning. This is due to the cases of complex usage by the young generation, making it the best choice for young people. Even though academics encourage social media for formal and informal learning, this research needs to be adequately theorized in most cases. People keep referring to sites and online applications to describe activities like socializing, collaborating, and publishing that make it easier for members to participate in different groups. Most social media platforms are programmed with features where users update their information on profile pages to promote their online existence. Connecting with other users using news feeds and sharing user-generated materials are some features of social media (Czerkawski, 2016). This made it easier for pages to be updated. Most college students use social media sites such as YouTube. Informal learning, known to be unstructured, is also as important as formal learning.
2.7 Factors That Affect YouTube SEO
It is not simple to get to the top of the rankings. World-renowned video producers and marketers compete for the top spot-on YouTube (Funk, 2021). SEO on YouTube is the result of many things. Several factors, including the following, influence YouTube SEO:
Table 1
Table showing the Factors of YouTube SEO
Search for Keywords
The most significant aspect of YouTube SEO is keyword research (Nyagadza, 2022). Users of YouTube search for many different terms, which marketers should identify. Keywords should be used as the basis for creating videos. When creating a video, it is essential to include keywords in the title, description, and tags (Jaffari, 2019).  In a video, you should employ both exact matches and comparable keywords. Consider the term search volume while choosing keywords for video.
There will be limited views for keywords with low search traffic. As a result, keywords should have a high volume of searches. The YouTubers can determine search volume using commercial programs such as Ahrefs.com and Semrush.com (Jaffari, 2019).  With the help of YouTube’s suggested videos, it is easy to develop keyword suggestions. For example, a YouTuber has a topic about adding a Google Doc in Google Drive. Therefore, the keyword here is a Google document. When we put it on one of the sites, several words will be presented as a question, a sentence, or general words. According to the YouTube trend videos and what some YouTube users are looking for: how to add a Google document in Google Drive? How to add a Google Doc, Google Doc secrets, and more (Nyagadza, 2022). YouTubers can search for keywords by watching free popular videos in the same area.
Keyword Search should be considered when developing any new video to determine the level of competition for a keyword (Jalal, 2020). It is necessary to use keyword research tools to identify the keyword. If there is much competition for the term, ranking the video will be challenging (Bennett, 2022). Therefore, it is necessary to locate a different keyword. There must be a strong focus on the video keyword and an actual amount of information on the keyword in the video. Even if YouTubers can utilize multiple keywords, it is best to stick to just a handful throughout a video.
According to Cowley (2020), SEO tends to convey well-adjusted and arranged data on user search nature, web search algorithms, competitive circumstances, and media creation. Web pages, videos, and images can all be enhanced to boost their website’s rate for relevant keyword searches, leading to more traffic on the search engine (Berman and Katona, 2013). Therefore, to maximize the use of SEO strategy, content creators concentrate on the video text in closed captions, transcripts, and subtitles, improving user engagement, experience, viewership, and watch time (Jalal, 2020). Previous literature has concentrated on strategies used for YouTube SEO, such as naturally inserting keywords into the video title (see Figure 3). Using keywords for the video’s title will determine the traffic attracted to the video and convince the viewer to click and watch the video.
Figure 3
Image showing the % of videos with a keyword-title match on YouTube.
Even though many videos have almost similar titles, specific Keywords are inserted to differentiate the videos from the rest. Videos with title matches are ranked first to fifth with a naturally added keyword on the title. However, videos with a lower percentage that matches the video’s title get ranked in lower positions, which impacts the number of viewers, as it is known that users mainly consider the first three or five videos that appear after a search.
Duration Of the Video
As a video-sharing site, YouTube allows users to submit videos for up to 12 hours (Miller, 2021). As a rule, shorter videos are deemed less instructive. While creating the videos, the creator should keep this in mind. The film should be longer and more informative if it contains detailed information (Jalal, 2020). It is essential that the video provides detailed information, and that the viewer feels satisfied after seeing it. YouTube video users should upload long videos which is at least 10 minutes or more to get a large number of watch hours (Jalal, 2020). This makes YouTubers fulfill one of the main conditions, which is 4,000 watch hours (Miller, 2021). YouTubers should avoid unnecessary lengthening of videos since this will distract viewers’ attention and could decrease search ranks (Jalal, 2020). YouTubers should avoid small videos at the beginning of their channel because they are not as prevalent on YouTube as longer ones unless the YouTubers utilize small videos as short. YouTube Shorts is a YouTube app feature that competes with TikTok and Instagram Reels which began its initial testing phase in India in September 2020 (Admin, 2020). YouTubers could watch and make 15-second videos with musical overlays using the beta feature (Drivas et al., 2020). It is best to avoid making videos longer than needed, reducing the audience’s interest.
Retention of The Audience
A viewer’s retention rate is the percentage of a video viewers have watched (Santiago, 2021). We can find it by looking at a portion of the video shows in search. When viewers do not like the video from the beginning, they pause it and prefer to move on to another video since the result is low retention. A high retention rate is achieved when viewers watch the entire video to ensure optimal retention. The footage must live up to the viewers’ expectations. Those who find the film does not meet their expectations will exit the video. The creator should make sure that the video matches the title and keywords. If the video’s title does not match the content of the video, viewers will exit the video. Clickbait is the period given to video titles and thumbnails with little, if anything, to do with their videos; because of this, people unexpectedly lose pastime in channels that regularly use clickbait, as most are not keen on blatant false advertising (wdc_dev, 2022). No clickbait in the thumbnail! It should not make deceptive claims in the title or thumbnail. Click bait thumbnails have been shown to have a low audience retention rate, according to research.
Enticing videos are essential for good audience retention. Throughout the video, the viewer should be given something of value to take away. While watching the video, the viewer should not get bored. YouTubers can introduce some surprises in the middle or at the end of the video to make it more interesting. For example, it is adding a trending side topic around the topic and excitingly crafting the content. There must be no stuttering in the dialogue, so a viewer does not lose the pleasure of sequential follow-up of the subject (Jalal, 2020). Likewise, a question should be asked and answered at the end of the video by YouTubers. As a result, viewers will be more inclined to watch the video to the very conclusion. The video’s popularity on YouTube is ranked by how enticing it is to the audience. According to YouTube’s algorithm, variables such as the total number of views, how fast they are growing, and where they originate from are considered when calculating rankings (Jaffari, 2019). YouTube will select videos most relevant to users and representative of the platform’s large viewership.
Keeping YouTube Viewers Engaged
YouTube favors videos that keep users on the platform (Tafesse, 2020). If viewers stay on YouTube, they will watch more videos, resulting in more advertising being presented to them and more income collected by YouTube. YouTube could lose money if a video encourages viewers to switch to another platform after watching the video.
To retain viewers on the YouTube platform, creators should aim to keep them engaged (Tafesse, 2020). Of course, YouTubers should know what subscribers want from the content provided to them on the channel, by asking or sharing them in the comments so that the audience feels comfortable while watching the videos. Lack of engagement is a downside to YouTube if the videos encourage viewers to switch to another venue after watching the videos. As a result, the creator can direct viewers to other videos they have produced on YouTube. The engagement will keep the viewers on the platform and increase the number of people who watch the YouTube channel due to the expanded viewership (Jalal, 2020).
Competitions
Search engine optimization relies heavily on competition in a particular niche or for a specific term to be successful (Swapna and Anuradha, 2018). Rank video is challenging if there is much competition for the keyword. Much effort is necessary to rank the video. If the term has a low level of competition, the rating of the video is relatively straightforward (Seo and Jung, 2018). Creators should look at the competition in their niche for a specific period. YouTubers can use paid YouTube SEO tools such as Keywordtool.io, Kparser, and Keyword Keg to examine keyword competitiveness. As we have indicated, search engine optimization tools are very many, some of which are similar in function, and others are different. There are various SEO tools available, some of which are free and others of which are expensive. Of course, SEO tools assist channel owners in enhancing the visibility of their videos in search results and the quality of the pages of their channels (Matošević et al., 2021). These tools make the work of channel owners much easier because they enable YouTubers to get All the data for their videos, which, if they use it intelligently, will be able to lead the search results.
Search engine marketing specialists can mix quite a few tools such as Google Keyword Planner to gather raw data, Google Search Console to reveal their sites, and Google Analytics (jouri, 2022). Then motel to enterprise Genius tools such as Power BI and Pentaho to analyze the obtained data. However, the problem is that this approach is not extraordinary in ingesting time and sources and is limited by using Google, which, although dominant in the search engine market, is no longer the sole one. Search engine optimization tools come to replace manual data series and analysis, making the work of search engine optimization gurus more professional and higher at the usage of valuable time. As a rule of thumb, starting with keywords with little competition is best. Targeting high-competition keywords after a certain amount of video-making experience is possible.
Before creating a video, it is essential to research the competition. Because it is easier to thrive in niches with less competition, less competitive niches should be prioritized. Getting high rankings for more competitive keywords is not impossible. Videos with high-end video and audio quality and those with a low bounce rate, a high Click Through Rate CTR, and others can still rank well even in increased competition (Hendricks, 2022).
Like, Comment and Share
Likes to indicate to YouTube’s algorithm that viewers are linking to a particular video, resulting in a higher ranking in search results for that video (Costales et al., 2021). A video’s engagement is demonstrated when viewers leave a remark or share the video on social media. In other words, the video has exciting material. As a result, the visibility of videos will grow in more search results at better rankings. Some viewers mostly do not seem to leave a like, comment, or share a video. YouTubers should always encourage and ask their viewers to subscribe, like, and share in each video in a different way (Jalal, 2020). For example, YouTubers can say at the beginning, middle, or end of the video, don’t forget to click like so that the clip spreads to as many viewers as possible, and share your opinions or suggestions via the description box below the video. As well, you can share the video to your friends or family members so they can benefit from the information. A video’s popularity can be increased by using innovative tactics such as asking questions and sharing personal stories.
The Description of The Video
The creator of the video describes the video in written form. As a result, viewers will be better able to appraise the film and decide if they want to watch it or not. A video’s description can help viewers get into the video’s mood. Use keywords in the description when composing it. There is no limit on how many times a keyword appears in the report. A simple and easy-to-understand explanation is ideal for viewers. When writing an illustration, it is essential not to focus just on the YouTube algorithm (Miller, 2021). It should provide information on what is being viewed and described. YouTubers must include a few keywords relating to the video in the video description to make it more effective. Relevant exact match keywords can help you rank your video quickly. It is recommended that writers avoid broad keywords in favor of precise ones. As a result of the video’s popularity on YouTube, it is ranked by how enticing it is to the audience (Hingoro and Nawaz, 2021). YouTube will select videos most relevant to users and representative of the platform’s large viewership. It is crucial to research and implement keywords while optimizing video material. Get started today by implementing these YouTube video ranking tactics (Jaffari, 2019).
Theoretical Framework
YouTube videos and channels are optimized for better YouTube results using YouTube SEO. A complicated algorithm determines videos’ ratings on search engines like YouTube (Palanisamy and Liu, 2018). Content creators may improve essential metrics like follower count, brand visibility, website visits, and revenue by optimizing video content for YouTube and other video-sharing sites. YouTubers should optimize their YouTube channel’s page and its videos, playlists, metadata, and descriptions as YouTubers (Miller, 2021). Their videos can benefit from being optimized for YouTube and other search engines. Optimizing their video for YouTube depends on how many viewers their video has reached and how well the footage performs against other videos in its category. Google, Bing, and other search engines can help users find their videos.
Selective exposure theory is a model that explains how individuals choose to expose themselves to certain types of information and media while avoiding other types. The theory suggests that people seek information and media that align with their existing beliefs, attitudes, and values while avoiding information that contradicts or challenges them. In the context of YouTube, selective exposure theory can help to explain how content creators adapt to the specific environments they desire. Many YouTubers express their opinions and share their vision, self-evaluation, and cultural experience through the content they create (Papagiannis, 2020). However, how they adapt to their desired environment may differ depending on their cognitive dimensions and the platform layers they interact with. For example, some YouTubers may rely on scientific and research data to support their opinions, while others may not. This difference in the use of research data can be explained by cognitive dimensions such as critical thinking and analytical skills. Additionally, some YouTubers may interact with platform layers that focus on a specific topic or niche, while others may interact with more general platform layers. This difference in platform layers can be explained by the YouTubers’ values, beliefs, and interests.
Furthermore, selective exposure theory can explain how YouTubers adapt to their desired audience. For example, some YouTubers may create content that aligns with the values and beliefs of their target audience, while others may create content that challenges their audience’s perspectives. The content creators’ goals and intentions can explain this difference. In summary, selective exposure theory is a model that explains how individuals choose to expose themselves to certain types of information and media while avoiding others. Understanding their cognitive dimensions, platform layers, and audience helps explain how YouTubers adapt to the specific environments they desire. By understanding selective exposure theory, we can better understand how YouTubers create and share content on YouTube.
The author discussed how the study uses selective exposure theory to process new or contentious information. The idea of cognitive dissonance, which is the state of feeling psychologically uncomfortable, has a connection to selective exposure theory (Site, 2022). This contentious typically occurs when someone is given the knowledge that prompts them to argue it internally. Understanding the components that make up a SEO role is necessary for content creators to comprehend how selective exposure theory functions. These consist of attitudes, values, opinions, and convictions (Site, 2022). First, a belief is an opinion based on how we experience the world practically. This belief means that everything people experience, hear, see, touch, taste, feel, and everything else helps us from our views. Second, A value is a thing that a person places the most importance or stress on (Site, 2022).
Views, Channel Subscribers, Video Comments, and Estimated Watch Time are just a handful of the interaction markers influencing a video’s YouTube ranking (Pedotto, 2018). The search engine examines user experience measurements to determine the information’s quality (Wdc_dev, 2022). YouTube will rank videos with a higher level of engagement higher than those with a lower level of interaction. YouTube SEO can have an indirect impact on these variables. YouTube considers video attributes like title, description, and transcript when determining which videos to show in response to a search. These features are improved due to YouTube SEO, helping the video found for relevant keyword searches on the platform. The higher a video’s organic rating, the higher the number of views and engagement.
Methodology
3.1 The Scope of Study
YouTube is one of the most significant search engines hosting 2 billion users worldwide (Mohsin, 2022). YouTube gets about 500 minutes (about 8 and a half hours) of added content uploaded every minute, affecting its popularity with 90% of people using these social media platforms to discover contemporary brands and media, e-commerce opportunities have increased exponentially (Jackson and Baum, 2011). Every YouTube success story begins with sharing content; YouTubers produce content that viewers can easily relate to and enjoy. As their views rise, they get subscribers and new perspectives and thus increase their income levels when they achieve the two most important pillars of YouTube conditions. YouTubers must reach 1000 subscribers in the channel, and they must complete 4000 public watch hours on their channel to enable YouTubers to generate income (jouri, 2022). Therefore, this study focuses on problems for YouTubers who suffer from the weakness of their channels the essential workers, subscriptions, and views—and provides search engine optimization services and analyzes the YouTube channel accurately to know the secrets of (SEO). However, SEO techniques will significantly benefit these brands. Using search engine optimization (SEO) techniques, brands and organizations can reach a wider audience and improve their online presence, thus producing engagement videos (Khan and Mahmood, 2018).
The first step in YouTube SEO is to analyze keyword data. New web videos are being produced daily, making it essential to keep up with trends. YouTube can be established as a search engine within Google Trends to identify terms and words that influence the popularity of this video platform (Berman, 2013). However, Google Trends does not provide data and information on search volumes. Instead, the RPI is applied using TubeBuddy Keyword Explorer. It includes information about what is being searched for on YouTube concerning a brand or organization. Like website content, search engine data can particularly affect video ideas. Search Engine Optimization (SEO) tools can provide YouTube with search data that will help content creators capture the interests of their viewers and, consequently, lead to high video engagement (Cowley, 2020).
3.1.1 The scope of the study will encompass the following:
What do individuals YouTubers do to improve audience retention? 
What tools do they find most useful?
It is essential to note that with research, different strategies have been identified that can help YouTubers leverage SEO techniques to answer the following questions. For instance, according to a study, to increase the popularity of your videos and consequently promote engagement, the first thing YouTubers are advised to do is to select a great keyword. Most consumers navigate YouTube using keywords; therefore, employing a catchy phrase is most likely to capture the viewers’ attention and consequently influence YouTube engagement (Castronovo & Huang, 2012). Secondly, it is advisable to include accurate and closed captions. One crucial factor in understanding is that the viewers want authenticity; they want to trust that the video will answer most of their bugging questions when they press on a particular video. Therefore, including a caption that does not align with the contents of the video may be disengaging and lead to a loss of viewers.
YouTube is a universal platform accessed by billions of people from all over the world. With the important levels of diversity, people are bound to have diverse cultures, languages, and beliefs. Therefore, it is necessary to be language sensitive to attract a large audience. Another factor to consider with SEO techniques is that YouTube users can offer subtitles in multiple languages. In this case, the viewers can understand the contents of the video and engage with it, which is a positive factor for the YouTuber. With SEO techniques, the focus is on user engagement. Understandably, user engagement is one of the most vital elements when succeeding on YouTube. Therefore, leveraging SEO will benefit the organization/individual in diverse ways.
3.2 Sampling Strategy
This study’s sampling strategy involves using the stratified random sampling method. This method aims to select a sample population that is representative of the population being evaluated (Acharya et al., 2013). In order to achieve this, the data will be distributed and stratified into homogeneous layers, and random samples will be selected from each stratum for analysis. The participants for this study will be 200 YouTube content creators or YouTubers. These participants will be selected based on specific criteria such as the number of subscribers, the content they create, and the level of engagement with their audience. These criteria are critical because they will ensure that the sample population is representative of the studied population.
It is important to note that this study will focus on content creators with a minimum of 1,000 subscribers. This threshold was chosen because it ensures that the participants have a significant level of engagement with their audience and are actively creating content on the YouTube platform (Dean, 2017). Additionally, the study will only include content creators producing original content and not using fake subscribers or other illegal methods to inflate their subscriber count artificially. This is important because it ensures that the data collected is accurate and reliable and that the actions of a small group of individuals do not skew the results of the study.
The study will also focus on creators creating content in a specific niche or industry. For example, some categories may include beauty and fashion, technology, gaming, and lifestyle. This focus will allow the author to analyze the specific SEO practices and techniques YouTubers use in these categories and how they impact their success on the platform. Participants will be contacted through various social media programs, email, and text messages. They will be asked to complete a survey link sent to them through these methods. The survey will be conducted using the Qualtrics survey site. It will include questions about the participants’ content submission process, the use of SEO tools, and their audience’s engagement with their channel. This survey will be designed to gather information about content creators’ obstacles when submitting content to the YouTube platform. The study will also investigate how these obstacles affect the creators’ ability to create quality content and engage with their audience.
It is important to note that there has been much research on SEO and YouTube, but there are still questions about the challenges some YouTubers face (Dean, 2017). For example, there have been reports of YouTubers buying fake subscribers to inflate their subscriber count artificially. This study aims to investigate these issues and provide a more comprehensive understanding of content creators’ challenges on the YouTube platform. The study will also examine SEO’s impact on different content creators. For instance, the study will analyze the impact of SEO on established YouTubers with a large subscriber base and new YouTubers who are just starting. This will help to understand how SEO can benefit YouTubers at different career stages. The study will also investigate the impact of SEO on the different categories of YouTube videos. For example, the study will examine how SEO practices differ between beauty and fashion, and technology YouTubers. By analyzing the specific SEO practices and techniques YouTubers use in different categories, the study will identify best practices and recommendations for improving SEO in specific niches. The data collected for this study will be analyzed using various statistical techniques, including descriptive statistics and correlation analysis. These techniques will allow the author to identify patterns and trends in the data and make inferences about the impact of SEO on YouTube.
The results of this study were presented in various formats, including figures, tables, and charts. Using different formats will make it easy for the reader to understand and interpret the study’s results. Overall, this study aims to provide a comprehensive analysis of the impact of SEO on YouTubers and their success on the YouTube platform. By analyzing data on the specific SEO practices and techniques YouTubers use in various categories, the author hopes to identify patterns and trends to help other YouTubers improve their SEO and increase their success on the platform.
This study benefited YouTubers looking to improve their SEO and increase their success on the platform. The study was also beneficial for businesses and marketers looking to advertise on YouTube. By understanding the impact of SEO on YouTube, businesses and marketers can make better-informed decisions about how to advertise on the platform. The results of this study were published in academic journals and presented at conferences, making the findings available to a broad audience. Additionally, the study’s findings were made available to the public through a website, allowing anyone to access the results and use them to improve their SEO and increase their success on the YouTube platform.
3.3 Research Design
This study is exploratory research where various factors relevant to the hypothesis have been observed. In this case, the research aims to determine the impact of YouTube search engine optimization to understand how this research can be classified as exploratory research. It is necessary to define its components. Experimental research can be defined as a research process used to investigate a problem that is not clearly defined. It is done to promote a better understanding of an existing problem. In this research, a specific idea was identified to better raise the level of YouTubers’ channels with accurate details to make their channel videos in the first list of the search engine. Optimizing the YouTube search engine, identifying related issues such as how to increase engagement, deal with tags, improve content performance, and others. The author determined this research design because the problem is still in its initial stage and is based on theory and explanatory research based on the results obtained.
The first step in exploratory research is to create a hypothesis. It is vital to understand that previous research has been conducted on this issue; as a result, this is not a relatively new research process (Lawrence & McKenzie, 2000). Therefore, the hypothesis was easily formed by analyzing the questions presented at the beginning of the research process. That is, what is the impact of YouTube search engine optimization? The study aimed to answer what, why, and how. For example, how does search engine optimization affect YouTube engagement? What is the impact of using YouTube SEO as a social platform? These were some of the questions that had to be answered once the writer did the research. Therefore, taking an exploratory research design was critical.
The effect of the specific research design is based on the data collection methods implemented. In collecting data, surveys will be used. It is essential to understand that this activity will be online only to provide sharing of a large sample size. The research allowed the audience to offer their thoughts and opinions, consequently influencing the results’ reliability (Sharma et al., 2020). The pool of respondents will be determined later, as about 200 participants will be included in the research process. I randomly selected this group to influence the reliability of the results obtained. The survey and interview questions will be closed and open-ended to allow the participant to choose what to answer and what not to answer. The study will respect the participants’ level of knowledge and information and push them to provide direct and accurate answers.
The study methodology also included the use of the literature review section. A literature review is one of the costliest ways to discover and provide answers to the hypothesis developed. An important to note that this research has been done before. With the popularity of search engine optimization, various researchers have been interested in understanding how this affects social media platforms and how algorithms can leverage them to influence success. The author will evaluate information from online sources, libraries, and commercial databases to establish a connection with the research. In this case, the study focused on determining how search engine optimization affects other platforms and whether the effect can be likened to YouTube. In addition, various factors related to search engine optimization will be identified and included in the research to influence the reliability of the information collected in this section which will determine the research hypothesis.
Data Collection
The target audience for this study is content creators. They are more suited to respond to this study because they are adults and are assumed to be of sound mind and able to provide reliable data (Vaish et al., 2022). The author will ask YouTubers to fill out an online survey through Qualtrics that includes a certain number of questions depending on the selection method. The questions will be divided into several sections. This data collection will consist of questions about YouTube search engine optimization:
What do individual YouTubers do to improve audience retention? 
What tools do they find most useful?
Some YouTube users often suffer from the lack of videos on the platform’s channels. This problem is a concern for some YouTubers who spend most of their time on different technologies and preparing content before, during, and after publishing on the channel without activating several factors, the most important of which is optimizing its search engine. Through the questionnaire, the writer will collect information and will obtain several valuable pieces of information from about 200 people from different countries. The survey results may be shocking to some people because it is considered a phenomenon of content creators publishing without considering the various aspects of improving the channel. Also, attracting the target audience is deemed to be content topics such as education, entertainment, fiction, sports, and others.
The questionnaire will be developed and include between 20-40 questions for YouTubers and content creators through the program and the Qualtrics survey site. The survey questions will be published in many social media programs to reach the most significant number of the target audience and those programs that the author will use in the survey, such as Facebook, WhatsApp, Twitter, and others. The author will draw many different responses from the concerns of some YouTubers.
Instruments
The preferred method of data collection in this study is online surveys. Online surveys use specific computer programs to obtain the required data using a series of prompts and questions. In addition, online surveys are affordable, or they may be accessible and describe the characteristics of the target population. Online surveys also help build trust between the researcher and the respondents based on their anonymity. Building this confidence is critical to obtaining accurate responses from respondents. In addition, online surveys are more convenient as respondents can fill them out at their convenient place and time.
From this perspective, a YouTube search engine optimization survey will be conducted. Many questions will be written and phrased for the target audience. Furthermore, after confirming the validity of the academic question, a meeting with expert specialists from universities and training centers will give the green light to these questions raised in several aspects that serve the study. The focus will be on target age groups, approved behaviors that draw audiences to the channel, time spent on electronic devices, and information about YouTubers’ awareness and safety from suspicious operations. The question format focuses on YouTubers with educational content on the YouTube platform, emphasizing that YouTubers must provide answers with explicit consideration for this phenomenon. This format includes several different questions on the issue of SEO if YouTubers are distressed with the SEO process, hoping to solve this problem. It is also possible to choose the gender and age of the creator indicated in the survey. The study was organized and published by Qualtrics, a program specializing in designing professional surveys and measuring survey results in detail. Two types of variables will determine the questionnaire questions in the first part: the first is an independent variable, and the other is a dependent variable (Marie, 2019).
Variables are essential features of quantitative research. According to Creswell (2014), a variant is an observable or measurable trait of an individual, which varies among the individuals under study. Study variables in scientific research are everything that accepts measurement, and in this research the variables are quantitative measurement. Every matter that accepts change can be a variable. The most important thing that distinguishes variables are the elements of influence and vulnerability, and the researcher determines the relationships between all variables and adjusts all these relationships (Roopa and Rani, 2012). There is more than one type of variables in scientific research, and the characteristics of these variables differ according to their types, and the most important of these are independent and dependent variables (Marie, 2019).
The independent variables influence the results while the dependent variables depend on the independent variables; hence, dependent variables are the effects of the independent variables. Some of the variables for this research are age, gender, and attitudes to using YouTube for educational purposes. These variables are in line with the main research problem. The determination of the demographic questions in the questionnaire are independent variables: determining the content creators, the age of the participants, the gender category, uploading the content on the channel, the period for creating the content on the channel, and the length of time for the videos. As for the dependent variables: owning a channel on the YouTube platform, the content provided on YouTube, the concerns faced by content creators on YouTube, as well as the sources used in the content industry.
The independent variables
An independent variable is defined as a variable that performs all other variables but is not affected by any of them (Creswell, 2014). This type of variable measures a group of individual characteristics that are not affected by any other variables, such as gender, age, etc., and independent variables may affect a group of other variables sometimes. The questionnaire, specifically the demographic information in Appendix C, was collected by the principal investigator to determine the accuracy of the answers on the topic of the importance of YouTube SEO. These questions were used to examine the overall reliability of the content creators in this study. The questions were also used quantitatively to increase the opportunity for content makers to participate easily through yes-no and multiple-choice questions. Below are some sample questions:
Are you a content creator?
Please choose the correct category for your current age.
Please choose the correct gender category for you.
How often do you publish contents on your channel?
How long have you been working in content creation What is the average length of your YouTube videos?
Second: dependent variables
Dependent variables are considered one of the most important types of variables in scientific research, and this type of variable is closely related to other variables, and we will explain the matter with a well-known simple example. For video and other factors.
Do you have a YouTube channel?
Please select what content do you provide to your YouTube channel YouTube content? (Check all that apply).
What barriers have prevented you from uploading your YouTube videos to you channel are the barriers that prevent you from creating YouTube on to your videos into your channel: (Check all that apply).
What are the resources that you use to support your YouTube content? are used to support your YouTube content: Check all that apply.
The next step is to plan to collect many surveys by posting them on social media platforms, including Facebook, WhatsApp, Snapchat, etc. The author will ask content creators questions about their full knowledge of dealing with YouTube as an educational platform and how they use electronic tools according to their watch hours. It is important to know how much time they spend on different devices to increase knowledge about increasing the productivity of YouTube channels such as video courses, articles, technical news, and others. A wide range of YouTubers’ responses to the YouTube SEO issue will be collected and continued to be resolved. Everyone will receive the questions, and people can be interested in the questionnaire to explain the problems that may be an issue they face daily.
The instrument chosen for data collection depends on the data collection type. This quantitative research process will utilize surveys. Surveys were employed in this research process due to the multiple benefits. It can be gained by the author as well as factors brought up previously by the content creators and previous research. For instance, conducting this type of research requires financial resources. Surveys are relatively inexpensive compared to other data collection methods (Nyagadza, 2020). This study employed both online surveys to cut costs further. Administering surveys was also beneficial in that it was extensive. Surveys are helpful when describing the characteristics of a large sample population. The sample population in this research study was large, with approximately 200 participants. As a result, using surveys was beneficial in establishing a connection between the hypothesis developed and the answers sought.
Additionally, the survey was beneficial for this research process because it was flexible. It is vital to understand that the writer can administer surveys in different modes, including email surveys, social media surveys, and paper surveys (Roopa & Rani, 2012). In this research, the writer will utilize online surveys to obtain information from participants in different geographical locations. Additionally, it provides the participants with convenience and the flexibility to answer the questions in the comfort of their homes. The online survey’s anonymity allows respondents to respond more candidly and openly. There is no pressure on the participants to answer in a certain way. In addition, they do not feel like they are in a corner when answering the questions asked. This lack of pressure affects the accuracy and credibility of the results obtained, positively impacting the research process.
Procedures
Many YouTube channel owners, especially beginners, aim to increase the number of views on their videos. Despite the effort put into creating meaningful and helpful content, many channel owners are disappointed when they find that their videos have only received a limited number of views (Pedotto, 2018). This can often be attributed to a need for more reliance on YouTube SEO, which represents a set of steps that can be taken to increase the views of videos with minimal effort (Nyagadza, 2020).
The study procedures include the approval of the IRB for the questionnaire, which will be presented with unanticipated numbers of no less than 200 responses by the participants, who are the content makers on social media, specifically the YouTube platform. Of course, the participants will be told through Appendix A, which was attached to the end of this research, about the period it takes to take this survey, which is within 3 to 5 minutes. Also, Appendix A will provide copies of the survey consent form page, which will be strictly confidential. Appendix C will be about demographic and search engine optimization via YouTube questions. Information will be collected by publishing the survey link through the researcher’s social media accounts and social media groups among content creators. Participants should not register through e-mail or record personal information while completing the questionnaire.
In this research, I will examine the success of search engine optimization procedures in several key factors by implementing a plan on a YouTube channel. These critical factors include:
Search for Keywords (Source: Google Keyword Planner)
Duration of the Video (Source: YouTube Analytics)
Retention of the Audience (Source: YouTube Analytics)
Keeping YouTube Viewers Engaged (Source: Questionnaire on viewer engagement)
Competitions (Source: Questionnaire on viewer engagement)
Like, Comment, and Share (Source: YouTube Analytics)
The Description of the Video (Source: Questionnaire on the effectiveness of video descriptions)
In order to test the effectiveness of these factors, we will be using multiclass logistic regression as a statistical method. The questionnaire used in this research will be sourced from previous studies on YouTube SEO and has been tested for both reliability and validity. Furthermore, it is essential to note the significance of YouTube in education. The results of this research can significantly impact how YouTube can be utilized to improve education (Tuğan, 2021). By understanding the success of search engine optimization procedures in crucial factors, educators and content creators can better understand how to make their educational videos more easily discoverable and engaging for students. This can lead to more effective use of the platform for education and ultimately improve the learning experience for students.
Data Analysis Methods
The first process in data analysis is data preparation, which aims to convert the data obtained into something comprehensible and readable (Lawrence & McKenzie, 2000). The data for this study followed a four-step process: determining fraud, screening, procedural checks, and completeness checks. This procedure guarantees that the data gathered is precise, impartial, and representative of the population being investigated. A random sample of YouTubers was chosen to create the sampling frame for this study. This sampling strategy was adopted to minimize the possibility of bias and guarantee that the sample represents the population being investigated.
The data validation process aimed to identify whether the data collection process was done according to the developed standards and without bias to eliminate discrimination in the data collection process. This process involved checking the data for errors, inconsistencies, and outliers to ensure that the data was accurate and reliable. Additionally, the data analysts were also chosen randomly to reduce the chances of bias further when analyzing the results obtained.
In terms of data analysis, both inferential and descriptive statistics were used. The descriptive statistics included averages and percentages, which helped provide the face value of the collected data for further analysis. Descriptive analysis also helped summarize factors that positively impacted YouTubers while following SEO procedures. Statistical analysis was also used to determine the percentage of people who benefited from SEO tools (Roopa & Rani, 2012). This analysis is the first level in the data collected (Abt, 1987). Descriptive analysis summarized the data and identified patterns the author can use to interpret the results. However, it is essential to understand that descriptive analyses are most beneficial when the results obtained do not need generalizations (Lawless & Heymann, 2010). Correlation analysis also analyzed the interactive factors used while uploading a video to the YouTube platform. This analysis helped determine the association between independent and dependent variables. A t-test was also used to determine the statistical significance of each independent variable (Saldaña, 2014). This test helped in the analysis of independent and dependent variables.
The analyzed data from descriptive statistics was presented in figures, tables, crosstab tables, pie charts, and bar charts, while inferential statistics was presented in tabular form. The frequency of practical sessions and surveys were necessary for many factors during the YouTube content creation process. Survey questions contributed significantly to frequency analysis. The author used the synchronous encoding method to encode the practical optimization sessions (Dean, 2017). This information is then used to disprove or support the hypothesis that YouTubers have a positive impact through SEO. The diagnostic analysis was carried out to determine how certain practices among YouTubers lead to adverse effects through subscription sharing and illegal methods to eliminate bias in the data collection process.
This study aims to analyze the impact of SEO on YouTubers and their success on the YouTube platform. Regarding categories of YouTube videos, the author focused on YouTubers who create content related to a specific niche or industry. For example, some categories may include beauty and fashion, technology, gaming, and lifestyle. This focus will allow the author to analyze the SEO practices and techniques YouTubers use in these categories and how they impact their success on the platform (Papagiannis, 2020). By analyzing data on the specific SEO practices and techniques YouTubers use in various categories, the author hopes to identify patterns and trends to help other YouTubers improve their SEO and increase their success on the platform.
It is important to note that while this study focused on the impact of SEO on YouTubers, it also considered other factors that may influence YouTube’s success on the platform. For example, the study considered the impact of YouTube’s content quality, audience engagement, and video promotion strategies on their success (Schaffner, 2019). The study also examined the impact of SEO on different types of YouTubers. For instance, the study analyzed the impact of SEO on established YouTubers with a large subscriber base and new YouTubers who are just starting out. This example helped to understand how SEO can benefit YouTubers at different career stages.
The study also investigated the impact of SEO on the different categories of YouTube videos. For example, the study examined how SEO practices differ between beauty and fashion YouTubers and technology YouTubers. By analyzing the SEO practices and techniques used by YouTubers in different categories, the study identified best practices and recommendations for improving SEO in specific niches (Papagiannis, 2020). The data collected for this study was analyzed using various statistical techniques, including descriptive statistics, correlation analysis, and t-tests. These techniques allowed the author to identify patterns and trends in the data and make inferences about the impact of SEO on YouTubers.
The results of this study were presented in various formats, including figures, tables, and charts. Using different formats will make it easy for the reader to understand and interpret the study’s results. Overall, this study aims to provide a comprehensive analysis of the impact of SEO on YouTubers and their success on the YouTube platform. By analyzing data on the specific SEO practices and techniques YouTubers use in various categories, the author hopes to identify patterns and trends to help other YouTubers improve their SEO and increase their success on the platform.
This study has benefited YouTubers looking to improve their SEO and increase their success on the platform (Edwards et al., 2021). The study also benefited businesses and marketers looking to advertise on YouTube. By understanding the impact of SEO on YouTubers, businesses and marketers can make better-informed decisions about how to advertise on the platform (Papagiannis, 2020). The results of this study were published in academic journals and presented at conferences, making the findings available to a broad audience. Additionally, the study’s findings were made available to the public through a website, allowing anyone to access the results and use them to improve their SEO and increase their success on the YouTube platform (Ferreira et al., 2023).
This study provided a comprehensive analysis of the impact of SEO on YouTubers and their success on the YouTube platform. The study focused on the specific SEO practices and techniques used by YouTubers in various categories and analyze the impact of these practices on YouTube’s success (Schaffner, 2019). By identifying patterns and trends in the data, the study was able to provide recommendations for improving SEO and increasing success on the platform. The results of this study benefited YouTubers, businesses, and marketers and contribute to the field of digital marketing and YouTube SEO.
4. RESULTS
This chapter highlights the results of the research study conducted to investigate the usage perception of YouTube Search Engine Optimization (SEO) among content creators. The study was guided by several objectives, and this chapter is organized based on those objectives. Objective one aimed to describe the demographic characteristics of the study participants.
4.1 Objective One:
The objective of this research was to investigate strategies employed by YouTubers to improve audience retention. The different variables in line with this objective has been discussed in the following sections.
4.1.1 Age
Participants in the survey were asked to provide their ages, which were subsequently grouped into distinct categories. The age distribution of the content creators is illustrated in Table 2 below.
Table 2
Age Distribution of Content Creators
Figure 4
Frequency of Age
The content creators aged 18-24 years (26.24%) constitute a substantial portion of the study with this demographic segment often characterized by their familiarity with digital trends and the new technology. This study anticipated to emphasize all visual appealing, fast-paced content and trends that resonate with younger audiences (Himma-Kadakas et al., 2018). In addition, their reliance on social media platforms offers valuable insights into maintaining audience engagement. The age group 25-34 years (27.72%) entailed all the participants but still take survey. Their strategies may be different and demonstrate a balance between creativity and expertise which reflects A diversified content approach that bridges the generational gap that exists. Analyzing their practices provides insight into creators’ transition from targeting younger demographics to engaging broader audience loyalty. Their insights into the different age categories brings the aspect of age specific strategies contributing substantially to understanding the goal of audience retention tactics among youtubers.
4.1.2 Gender
The study participants were also asked to indicate their gender. The distribution of gender among content creators is presented in Table 3 and .
Table 3
Gender Distribution of Content Creators
Figure 5
Age Distribution
The distribution of gender of content creators, as presented in Table 3 is substantial in investigating strategies to improve audience retention on YouTube. The predominant males 47.52% in this study highlight the substantial gender gap within this content creator community. Being able to understand the strategies employed by male content creators to retain and engage their audience is important for gender specific strategies towards content creation. The males prioritize certain types of content, storytelling types or engagement tactics which will resonate with their audience segment (Himma-Kadakas et al., 2018). In addition, females constituting about 36.14% of the content creator’s employee distinct strategies focusing on building connections, empathy, or community building aspects within their content. The presence of individuals who prefer not to indicate their agenda reported about 14.85% and a small category of other about 1.49% introduces complexity. The knowledge of this sort of participants offers important insights on how content creators navigate the audience engagement when gender identification is not clear. Altogether, the gender distribution arms important layer two achieving the objective through highlighting the potential gender related differences in strategies that were used for audience retention among youtubers.
4.1.3 Content Creator Status
Participants were asked if they considered themselves content creators. The distribution of responses is provided in Table 4.
Table 4
Content Creator Status
The distribution of content creator status as indicated in Table 4 is an important variable in investigating strategies to enhance audience retention among youtubers. Most of the survey participants 66.83% identify themselves as content creators while another 33.17% do not engage in content creation. The division highlights a key opportunity to explore different strategies that were employed by the two groups (Su et al., 2022). In considering those who are content creators, their content creation practices were identified such as content type, frequency of uploads, and engagement approaches they employ. Their strategies provide valuable information in what works best for retaining and engaging an audience on YouTube. A key question that was addressed is how content creators with a longer history of content production have employed different strategies compared to newcomers. The study also highlights why some participants do not identify as content creators which is an important component on audience retention such as what factors deter such individuals from engaging in content creation and the insights that might offer important information. The variable helps this study population and leads to a more comprehensive knowledge of different strategies that youtubers employ to improve audience retention.
4.1.4 YouTube Channel Ownership
Participants were asked if they had a YouTube channel. The distribution of responses is presented in Table 5.
Table 5
YouTube Channel Ownership
The variable YouTube channel ownership presented in the above table is very relevant when trying to understand the strategies that were used by youtubers to enhance audience retention. The survey participants have been separated into two distinct groups, those who own a YouTube channel accounting for 73.27% and those who do not about 26.73%. The differentiation is critical for evaluating and comparing the different strategies related to audience retention between the two groups. For individuals having YouTube channels different factors such as type of content they produce, video length, use of search engine optimization (SEO) approaches and audience engagement strategies are essential (Su et al., 2022). The specific SEO practices That Channel owners with higher audience retention rates employ was identified to play a major factor. In addition, the group who do not own YouTube channels provides different perspective pertaining the reasons for not participating in content creation. The study highlighted the factors that deter them from starting a channel and the relationship of the factors to audience retention concerns. The comparison allows a comprehensive evaluation of audience retention strategies and help to identify best practices for both content creators and then those individuals interested in improving audience retention on YouTube.
4.1.5 Length of Experience in Content Creation
The distribution of content creators’ experience levels in content creation is presented in Table 6 below.
Table 6
Experience Level
The variable “Experience Level” in Table 6 provides insights into the distribution of content creators based on their years of experience in content creation. This information is highly relevant to Objective 1, which seeks to investigate the strategies employed by YouTubers to enhance audience retention.
The largest group, consisting of content creators with less than a year of experience (39.60%), represents individuals who are relatively new to content creation. Understanding their audience retention strategies is crucial, as they may be experimenting with different approaches to engage and retain viewers (Wang & Chan-Olmsted, 2020). These creators might focus on producing high-quality content or actively seeking audience feedback to improve their videos.
Experience level variable presented in Table 6 offers distinct insights on their distribution of content creators based on their years of experience in content creation. The largest group consisting of content creators with less than a year of experience (39.60%) represented those individuals who are relatively new to content creation. This group employed different strategies because they were experimenting content creation (Wang & Chan-Olmsted, 2020). Content creators with one to five years of experience (42.57%) establish a great portion of the study population. Their strategies employed for retaining an audience are representative of a group that has had time to refine their content and audience engagement techniques. Furthermore, a smaller group of content creators with six to 10 years (12.87%) and 11 years or more (4.95%) of experience was of great significance. This sort of study population had unique insights into the long-term audience retention strategies, build and maintain visual loyalty and know how to curate content to fit the preferences of their audience.
4.1.6 Content Provided on YouTube Channel
The distribution of content types provided by content creators on their YouTube channels is presented in Table 7 below.
Table 7
Content Type on YouTube channel
The variable “Content Type” in Table 7 provides valuable insights into the types of content that content creators offer on their YouTube channels. This information is relevant to Objective 1, which aims to investigate strategies employed by YouTubers to enhance audience retention, as different content types may require distinct approaches to retain and engage viewers.
“Provide solutions” (20.56%) represents a substantial portion of the content provided by creators. These videos likely focus on addressing specific problems or answering questions, making audience engagement and retention strategies crucial. Creators using this format may rely on thorough explanations, clear problem-solving, and interactive elements like call-to-action (CTA) prompts to retain viewers.
The variable concept type in Table 7 offers important insights on the type of content that content creators offer on their YouTube channels. Different content types posted across YouTube requires distinct approaches to retain and engage viewers. The participant that responded to provide solution (20.56%) represent a substantial portion of the content provided by creators. The videos which likely focuses on addressing specific problems make audience engagement and retention strategies important (Wang & Chan-Olmsted, 2020). On lecturing explanations (23.06%) is a significant category that may create us to involve didactic methods to convey certain information. The audience retention in this case involves structuring content for clarity using visuals as well as encouraging viewers to stay engaged in the entire lecture. Monitoring viewer feedback and tailoring content based on audience preferences is very important in this case.
When making vlogs (19.17%) implies creators who share their daily lives across the media platform. Audience redemption vlogs are linked to personal connections and storytelling where creators use specific strategies such as maintaining consistent upload shadows, employing engaging narratives and illicitly engaging patients with comments and social media. Audio podcasts (9.17%) was an important auditory content where creators focus on engaging discussion, guest appearances and compelling storytelling. In addition, 11.67% of participants considered showing movies which is a content involving movie reviews, commentaries and analysis. Audience retention strategies involve detailed alliances, engaging critique and interactive discussions about particular futures in a movie. In this study, some participants choose to specify other 16.39% encompassing a variety of content types (Wang & Chan-Olmsted, 2020). This type of craters may employ diverse range of strategies based on specific content niches making it essential to explore their approaches diversely. Analyzing the distribution of content types in the context of providing insights into the diverse strategies employed by coordinate creators to announce audience retention is very important. Different content types are daylight and requires knowledge of the different variations that can help in providing A substantial recommendation for youtubers seeking to optimize audience retention.
4.1.7 Barriers Preventing YouTube Video Upload
The distribution of barriers that prevent content creators from uploading videos to their YouTube channels is presented in Table 8 below.
Table 8
Barriers Preventing Upload
The variable barriers preventing upload presented in table 8 highlights the challenges faced by content creators while uploading videos to their YouTube channels. Understanding these barriers is a critical component in building a context to evaluate the strategies employed by youtubers to improve audience retention (Altman & Jiménez, 2019). The results indicated low views 20.23% which is a common barrier encountered by content creators with the low views considered to be a demotivating factor that leads to greater questioning the value of their content. Some of the strategies suggested to eliminate this barrier includes optimizing video includes optimizing video titles, use of thumbnails, and descriptions to improve visibility. On low interaction 20.92% the viewers do not engage with content such as liking comment commenting or sharing the videos. The studies suggest that to address this challenge, content creators should focus on encouraging viewer interaction using call to action prompts within the videos, engaging with comments, and enhancing a sense of community around the channels.
Low interaction 20.92% indicated different challenges where viewers do not engage with content including liking, commenting, or sharing videos. Poor performance 13.33% could encompass a few aspects such as law watch time or high video abandonment rates. On content quality 13.79% is an important factor that affects the audience retention. The barrier can be eliminated by content creators focusing on improving the overall quality of the videos such as better production, clearer video, and engaging visuals (Altman & Jiménez, 2019). Technical equipment used 12.41% indicate challenges related to availability or quality of filming and editing equipment. It is evident that YouTube Greta has faced this barrier and needs to prioritize investing in better equipment. On Internet weakness 11.95% is a limiting factor that impacts upload speed and video quality. The participants that considered to respond with other 7.36% indicated unspecified barriers showing them diversity of challenges content creator space. In summary, the knowledge of barriers preventing youtubers from uploading videos is important to understand audience residential studies. Addressing such barriers involves several techniques such as technical improvement, content quality enhancement, and audience engagement approaches.
4.1.8 Resources Used to Support YouTube Content
The distribution of resources used by content creators to support their YouTube content is presented in Table 9 below.
Table 9
Resources Used
The variable resource used presented in table 9 offers important insights on the resources that content creators use to support their YouTube content (Altman, & Jiménez, 2019). The first resources that were used for social media 32.88% which is considered to be the most commonly used resource among content creators. Most content creators leverage social media platforms for content promotion and audience engagement. Creators share their videos on platforms such as Facebook, Twitter now known as x, Instagram and TikTok among others. These platforms offer a wide array of audience providing direct interaction with viewers. On websites 16.99% that content creators consider using is another important source. Family and friends accounted for 27.40% indicating that content creators rely on their personal networks for support. The support can be in form of assistance with video production, sharing content and providing feedback. Trend contents accounted for 15.34% reflecting awareness of trending topics and content formats. Content creators monitor different trends and niches and customize their content to fit the current happenings in the society.
Finally, 7.4% of the participant considered to answer with other which indicated that they consider different resources when creating their content. The knowledge of resources employed by content creators provide important information into their strategies for improving audience retention (Altman, & Jiménez, 2019). Effective use of resources involved a combination of promotional through social media maintaining websites leveraging personal networks steering current with trends and potential employees across the board. By using the resources strategically content creators can effectively engage their audience and improve audience retention on their YouTube channels.
4.1.9 Average Length of YouTube Videos
The distribution of the average length of YouTube videos created by content creators is presented in Table 10 below.
Table 10
Average Length of Videos
Figure 6
Average Length of YouTube Videos
The variable “Average Length of Videos” in Table 10 and Figure 6 provides insights into the video length preferences of content creators, which is relevant to Objective 1, aiming to investigate strategies employed by YouTubers to enhance audience retention. Video length is a crucial factor that can impact audience engagement and retention on YouTube.
The majority of content creators (32.67%) prefer to create videos with a duration of “1 – 10 minutes.” This duration range is commonly favored because it strikes a balance between providing substantial content and maintaining audience engagement (Phansab, 2018). Shorter videos in this range can be concise and to the point, while longer ones allow for more in-depth exploration of topics.
A significant portion (24.75%) of content creators creates videos with a length ranging from “11 – 20 minutes.” This range provides more time for detailed explanations or storytelling, which can help retain viewers interested in a deeper dive into the subject matter.
Videos with a duration of “0 – 59 seconds” (24.26%) are relatively short and may be used for quick updates, teasers, or attention-grabbing content. While they are concise, they can be effective in retaining viewers’ attention if the content is engaging and relevant.
A smaller but noteworthy percentage of content creators (10.89%) produce videos with lengths ranging from “21 – 30 minutes.” These longer videos may cater to audiences looking for comprehensive content or tutorials that require more time to cover. Finally, a portion of content creators (7.43%) opts for even longer videos, exceeding “31 minutes.” These videos are typically used for in-depth discussions, tutorials, or storytelling, which can attract and retain viewers who are highly interested in the subject matter (Phansab, 2018). In conclusion, the distribution of video lengths among content creators suggests that there is no one-size-fits-all approach, and creators adapt their video lengths based on their content and audience preferences. The choice of video length is a strategic decision that impacts audience retention, as it should align with the content’s depth, purpose, and the expectations of the target audience. Creators should consider their content goals and audience preferences when determining the optimal video length for improving audience retention on their YouTube channels.
4.2 Objective 2
To Identify the Most Useful Tools Utilized by YouTubers
4.2.1 Age
Participants in the survey were asked to provide their ages, which were subsequently grouped into distinct categories. The age distribution of the content creators is illustrated in Figure 7 below.
Figure 7
Age Distribution of Content Creators
4.2.2 Gender
The study participants were also asked to indicate their gender. The distribution of gender among content creators is presented in Table 11.
Table 11
The survey results reveal that the majority of content creators fall within the age categories of 18-34 years. Specifically, 26.24% of participants are between 18-24 years old, while 27.72% are aged 25-34 (al Nashmi et al., 2017). These two age groups together represent a substantial portion of the surveyed content creators. It’s notable that younger adults, aged 18-34, make up the core of YouTube content creators, which aligns with the platform’s reputation as a space where younger generations often flourish.
The age distribution also shows a decreasing trend in participation as age increases. Content creators aged 35-44 represent 25.74% of the sample, indicating that a significant portion of creators continue their content creation journey beyond their early thirties (al Nashmi et al., 2017). However, the percentage decreases further in the older age categories, with content creators aged 45-54 comprising 13.37% of the sample, those aged 55-64 making up 6.93%, and no participants aged 65 or older. This demonstrates that while YouTube content creation attracts a broad age range, it is more prevalent among younger and middle-aged adults.
Understanding the age demographics of content creators is crucial when identifying the most useful tools for YouTubers. Different age groups may have distinct preferences and needs when it comes to content creation tools, as they might have varying levels of familiarity and comfort with technology. Therefore, tailoring tool recommendations and strategies for YouTubers should consider the age-related nuances uncovered in this survey (al Nashmi et al., 2017). It suggests that tool developers and educators aiming to support content creators should consider designing tools and resources that cater to the preferences and capabilities of a diverse age range, with a particular focus on the younger and middle-aged demographics that dominate the YouTube content creation landscape.
4.2.3 Content Creator Status
Participants were asked if they considered themselves content creators. The distribution of responses is provided in Table 12.
Table 12
Content Creator Status
According to the survey results, a majority of participants, accounting for 66.83%, consider themselves content creators. This finding is crucial as it highlights the prevalence of content creators within the sample, indicating a substantial representation of individuals actively engaged in producing content for YouTube (Castillo-Abdul et al., 2021). These content creators are the primary target audience for tool recommendations and strategies aimed at enhancing their content creation processes and effectiveness.
On the other hand, 33.17% of respondents stated that they do not identify as content creators. This group represents a significant portion of the sample and may include individuals who engage with YouTube primarily as viewers or consumers of content rather than producers (Castillo-Abdul et al., 2021). Understanding this segment is essential for providing a comprehensive analysis, as the needs and preferences of viewers may differ substantially from those of content creators. For example, tools that support content discovery and user experience might be more relevant to this group.
The survey’s findings regarding content creator status lay a crucial foundation for the subsequent investigation into the most useful tools for YouTubers. It delineates the two distinct segments within the sample—content creators and non-content creators—each with its unique characteristics and requirements (Castillo-Abdul et al., 2021). This information will guide the development of targeted recommendations and strategies tailored to the specific needs of these groups, contributing to a more comprehensive understanding of the YouTube ecosystem and how tools can support content creation and consumption.
4.2.4 YouTube Channel Ownership
Participants were asked if they had a YouTube channel. The distribution of responses is presented in Table 13.
Table 13
YouTube Channel Ownership
The results show that a substantial majority, comprising 73.27% of the participants, indicated that they have a YouTube channel. This finding is significant because it highlights that the majority of the surveyed individuals are actively engaged in content creation or management on YouTube. As such, they are likely to benefit from recommendations and insights related to tools and strategies that can enhance their channel’s performance, content quality, and audience engagement (Parvinen et al., 2021). Conversely, 26.73% of respondents reported that they do not own a YouTube channel. This group represents individuals who may primarily consume content on the platform but do not actively create or manage their channels. For this segment, tools and recommendations may be more geared towards enhancing the viewing experience, content discovery, or engagement as viewers. Understanding the needs and preferences of this group is valuable for providing a well-rounded analysis of YouTube tool usage.
The distribution of YouTube channel ownership status among the survey participants provides a fundamental distinction in the sample, dividing respondents into content creators and non-content creators. This distinction is essential for tailoring recommendations and identifying the most useful tools, as the requirements and objectives of these two groups can vary significantly. Whether it is content creation, management, or consumption, recognizing the prevalence of YouTube channel ownership is a critical step in effectively addressing the diverse needs of YouTubers.
4.2.5 Length of Experience in Content Creation
The distribution of content creators’ experience levels in content creation is presented in Table 14 below.
Table 14
Experience Level Frequency and Percentage
In the context of Objective 2, which seeks to identify the most useful tools utilized by YouTubers, understanding the distribution of content creators’ experience levels in content creation, as presented in Table 6, is crucial. This data provides insights into the backgrounds and expertise of the surveyed individuals, which can influence their tool preferences and usage (Fischer et al., 2022). The results reveal that a significant portion, 39.60%, of content creators have less than a year of experience in content creation. This group likely includes newcomers who are still navigating the intricacies of YouTube and content production. For them, tools that offer user-friendly interfaces, beginner tutorials, and basic content management features may be particularly valuable.
A slightly larger segment, comprising 42.57% of respondents, falls into the 1-5 years of experience category. These individuals may have developed some proficiency in content creation but could still benefit from tools that enhance video editing, audience analytics, or SEO optimization (Fischer et al., 2022). A smaller portion, 12.87%, has 6-10 years of experience, indicating a more seasoned group of content creators. This cohort may have advanced needs, such as tools for audience retention strategies, monetization, and data-driven content decisions. Finally, 4.95% of content creators reported having 11 years or more experience. These individuals likely have a deep understanding of the YouTube ecosystem and may require specialized tools for advanced content management, audience engagement, and revenue optimization.
4.2.6 Content Provided on YouTube Channel
The distribution of content types provided by content creators on their YouTube channels is presented in Table 15 below.
Table 15
Conetent Type on YouTube Channel
Analyzing the distribution of content types provided by content creators on their YouTube channels, as shown in Table 15, is crucial for understanding the landscape of content creation on the platform, which aligns with Objective 2 aiming to identify useful tools for YouTubers (Wu et al., 2022). Providing solutions, chosen by 20.56% of content creators, signifies a prevalent genre where creators offer guidance, tutorials, or problem-solving content. Tools that can aid in research, data analysis, and content planning can be highly beneficial for creators in this category. They can help creators find trending topics, analyze audience needs, and structure their content effectively.
Lecturing explanation, selected by 23.06% of creators, suggests an educational aspect to their content. In this context, tools that facilitate presentation, lecture recording, and educational content design can be vital. Creators might benefit from software for creating engaging visuals, interactive quizzes, and online teaching platforms (Egodawele et al., 2023). Making vlogs, chosen by 19.17% of content creators, represents a popular format for sharing personal experiences and narratives. Video editing software, mobile apps for on-the-go recording and editing, and social media scheduling tools for promotion can be valuable resources for vloggers.
Audio podcasts, selected by 9.17% of creators, indicate a subset of content creators who primarily focus on audio-based content. Podcasting platforms, audio editing software, and distribution tools can assist creators in this category in producing and distributing their podcasts effectively. Showing movies, chosen by 11.67% of creators, suggests a potential interest in film analysis or commentary channels. Video editing software, movie analysis tools, and platforms for legal and ethical content sharing can be helpful for this niche. The category of “Other,” which accounts for 16.39% of creators, represents a diverse range of content types. Identifying specific tools for these creators would require a more in-depth understanding of their individual content needs and goals (Willemart & Steils, 2020). In summary, the distribution of content types on YouTube channels highlights the variety of content creators’ interests and niches. Recommending tools tailored to these specific content types can assist YouTubers in optimizing their content creation processes and enhancing the quality and effectiveness of their channels.
4.2.7 Barriers Preventing YouTube Video Upload
The distribution of barriers that prevent content creators from uploading videos to their YouTube channels is presented in Table 16 below.
Table 16
Barriers Preventing Upload
Analyzing the data on barriers preventing YouTube video uploads, as presented in Table 16, can provide valuable insights into the challenges content creators face. This analysis is particularly relevant in the context of Objective 2, which aims to identify tools that can assist YouTubers in overcoming these barriers. Low views and low interaction, reported by 20.23% and 20.92% of content creators, respectively, indicate a common struggle to gain visibility and audience engagement (Lim & Ang, 2023). Tools that offer analytics and audience insights could be immensely helpful. These tools can help creators understand their viewers’ behavior, tailor content accordingly, and develop strategies to increase views and interaction.
Poor performance and content quality, each cited by approximately 13% of creators, highlight the importance of maintaining high production standards. Video editing software, graphic design tools, and content optimization platforms can be indispensable for improving content quality and performance. Creators may also benefit from tools that offer content analysis and recommendations to identify areas for improvement (Lim & Ang, 2023). Technical equipment and internet weakness, reported by 12.41% and 11.95% of respondents, respectively, underscore the significance of the right hardware and a stable internet connection. Recommendations for tools that can test and enhance network connectivity, as well as assist in optimizing video settings for various devices, can be valuable in overcoming these barriers.
Finally, other barriers, mentioned by 7.36% of creators, represent a diverse range of challenges. Identifying and addressing these specific issues would require personalized solutions. Tools that offer flexibility and customization to meet individual needs might be particularly useful in such cases.
4.2.8 Resources Used to Support YouTube Content
The distribution of resources used by content creators to support their YouTube content is presented in Table 17 below.
Table 17
Resources Used
In the context of Objective 2, which aims to identify the most valuable tools YouTubers utilize, it is crucial to examine content creators’ resources to support their YouTube content, as presented in Table 17. Understanding the resources they rely on can shed light on the types of tools and strategies that may be most effective for these creators (Lim & Ang, 2023). Social media emerges as the most used resource, with 32.88% of content creators leveraging platforms like Facebook, Instagram, Twitter, and TikTok to promote and share their YouTube content. This indicates the importance of social media management tools, content scheduling platforms, and analytics tools to help creators optimize their social media presence.
Family and friends play a significant role in content creation, with 27.40% of respondents mentioning them as a resource. These individuals often provide support, feedback, and even collaboration opportunities. For content creators, tools that facilitate collaboration, communication, and feedback gathering from close circles could be invaluable (Lim & Ang, 2023). Websites are utilized by 16.99% of content creators, suggesting that many YouTubers maintain personal websites or blogs alongside their YouTube channels. Tools for website management, SEO optimization, and content integration could benefit this group.
Trends and trending content are essential for 15.34% of content creators. Tools that assist in trend analysis, keyword research, and content ideation can help creators stay relevant and produce content that aligns with audience interests. A smaller portion, 7.40%, mentioned other resources. These could encompass various support mechanisms, from professional networks to educational courses. Tools that cater to niche needs and personalized resources might be relevant to this group.
4.3 Objective 3
To Examine the Relationship Between Audience Retention Strategies and Content Creators’ Perceived Effectiveness
4.3.1 Age
Participants in the survey were asked to provide their ages, which were subsequently grouped into distinct categories. The age distribution of the content creators is illustrated in Table 1 below.
Table 18
Age Distribution Single Factor ANOVA
The results of the Single Factor ANOVA test, as shown in the provided table, offer valuable insights into the relationship between content creators’ ages and their perceived effectiveness in audience retention strategies. The central finding that age has a statistically significant impact on how content creators perceive the effectiveness of their strategies is noteworthy. This implies that different age groups within the content creator community may have varying approaches or perspectives regarding audience retention (Nicoli et al., 2022). The statistically significant difference in perceived effectiveness across age groups (F = 12.14, p < 0.00588) underscores the importance of considering generational dynamics when devising audience retention strategies on platforms like YouTube. Younger content creators, who are more digitally native and accustomed to rapidly changing online trends, might have strategies that align with the preferences of younger audiences. They may prioritize visually appealing, fast-paced content and interactive features to engage their viewers effectively. In contrast, older content creators might employ more traditional storytelling techniques and content formats that resonate with a different demographic. Therefore, understanding these age-specific differences in perceived effectiveness is crucial for content creators aiming to enhance their audience retention. Also, while the ANOVA test identifies a significant difference among age groups, it does not specify which age groups exhibit these differences. To gain a more comprehensive understanding, conducting post-hoc tests, such as Tukey's HSD (Honestly Significant Difference) or Bonferroni correction, would be beneficial (Holliman & Rowley, 2014). These tests can pinpoint which specific age groups have significantly different perceptions of effectiveness. For instance, it would be valuable to know if content creators in the 18-24 age group perceive their strategies as significantly more effective than those aged 35-44. Such insights would enable content creators to benchmark their strategies against age-related trends and potentially adapt their approaches accordingly. 4.3.2 Gender The study participants were also asked to indicate their gender. The distribution of gender among content creators is presented in Table 19. Table 19 Gender Distribution of Content Creators The marginally significant difference (p = 0.0532) implies that there may be some variance in how content creators of different genders perceive the effectiveness of their audience retention strategies. This suggests that gender could subtly shape content creators' strategies and perspectives (Holliman & Rowley, 2014). Besides, the F-statistic value of 5.77 suggests some variance between groups, but it falls short of being enormously significant. To gain a more comprehensive understanding, conducting post-hoc tests or examining specific gender groups' perceptions could provide clarity. These tests help identify whether male and female content creators differ significantly in their perceived effectiveness. This granular analysis can help content creators tailor their strategies to align with the nuances of gender-specific audience expectations and preferences. 4.3.3 Content Creator Status Participants were asked if they considered themselves content creators. The distribution of responses is provided in Table 20. Table 20 Content Creator Status The marginally significant p-value (p = 0.0979) implies that there might be some variation in how content creators perceive the effectiveness of their audience retention strategies based on their status. Content creators who identify as content creators and those who do not may have slightly different perspectives (Holliman & Rowley, 2014). However, it is essential to note that the p-value is quite close to the significance threshold. This proximity suggests that other factors could influence the observed difference, or it might be challenging to establish a definitive relationship due to the sample size. The F-statistic value of 8.74 indicates some variation between the groups but does not strongly support the presence of a significant difference. To gain a clearer understanding, conducting post-hoc tests or examining specific subgroups within content creators and non-creators may provide more conclusive insights. These tests can help determine whether there are substantial differences in perceived effectiveness among different segments of content creators and non-creators. 4.3.4 YouTube Channel Ownership Participants were asked if they had a YouTube channel. The distribution of responses is presented in Table 21. Table 21 YouTube Channel Ownership The Single Factor ANOVA results for YouTube channel ownership (whether participants own a YouTube channel) concerning their perceived effectiveness in audience retention strategies provide exciting insights. However, the findings are not statistically significant (Davis et al., 2013). The ANOVA test yields a p-value of 0.1659, higher than the conventional significance threshold 0.05, indicating no firm evidence of a significant difference. Let us delve deeper into these results. The non-significant p-value suggests no statistically significant difference in how individuals who own YouTube channels perceive the effectiveness of their audience retention strategies compared to those who do not own channels. In other words, channel ownership status is not decisive in determining perceived effectiveness in audience retention strategies. Secondly, the F-statistic value of 4.57 indicates that while there is some variation between the two groups, this variation is not substantial enough to reach statistical significance. This result implies that other factors or variables may have a more significant impact on perceived effectiveness in audience retention strategies than YouTube channel ownership status alone. 4.3.5 Length of Experience in Content Creation The distribution of content creators' experience levels in content creation is presented in Table 22 below. Table 22 Length of Experience The Single Factor ANOVA results for the length of experience in content creation concerning content creators' perceived effectiveness in audience retention strategies show some intriguing findings. While the p-value of 0.0389 is slightly below the conventional significance threshold of 0.05, indicating a potential statistically significant difference, further examination is required to draw meaningful conclusions. Let's critically analyze these results (Anjum et al., 2014). The p-value of 0.0389 suggests that there is a moderate level of evidence to support the idea that the length of experience in content creation might have a significant impact on how content creators perceive the effectiveness of their audience retention strategies. This indicates that there is a potential relationship between experience and perceived effectiveness in audience retention strategies. Besides, the F-statistic value of 6.93 indicates that there is a notable amount of variation between the groups based on different experience levels. However, it's important to note that this variation is not extremely large, which is reflected in the slightly below-threshold p-value (Anjum et al., 2014). Given these findings, content creators should consider that their years of experience in content creation may play a role in how they perceive the effectiveness of their audience retention strategies. Those with more experience may have refined their techniques over time, leading to higher perceived effectiveness. However, it's crucial to remember that while the results suggest a potential relationship, the effect size might not be substantial, and other factors could also influence perceived effectiveness. 4.3.6 Content Provided on YouTube Channel The distribution of content types provided by content creators on their YouTube channels is presented in Figure 8 below. Figure 8 Content Provided on YouTube To understand the relationship between audience retention strategies and content creators' perceived effectiveness, we can analyze the data related to the distribution of content types produced by content creators. The data categorizes content into six distinct types: "Provide solutions," "Lecturing Explanation," "Making vlogs," "Audio Podcasts," "Showing Movies," and "Other." A content type is a critical aspect of content creation strategy, as it determines the format and style of content creators produce. Each content type may require unique audience retention strategies to keep viewers engaged and returning for more. The data reveals that "Lecturing Explanation" is the most shared content type, accounting for 23.06% of the content created. Creators in this category likely produce educational or explanatory content, which may require clear and engaging explanations to retain viewers. Understanding the perceived effectiveness of retention strategies among these creators can show how effectively they communicate complex topics and maintain audience interest (Na et al., 2021). "Provide solutions" is the second most shared content type, making up 20.56% of the content. Creators in this category likely focus on problem-solving and providing valuable solutions to their audience. Analyzing their strategies and how they perceive their effectiveness in retaining viewers can offer insights into the effectiveness of educational content for audience retention. "Making vlogs" (19.17%) and "Showing Movies" (11.67%) represent content types that are more entertainment oriented. Creators in these categories may prioritize storytelling and engagement techniques to keep their audience interested. Investigating their retention strategies and perceived effectiveness can reveal how storytelling and entertainment impact audience retention (Geissler et al., 2021). "Audio Podcasts" (9.17%) represent a unique content type primarily focused on auditory content. Creators in this category may employ different strategies to engage listeners and keep them returning for more. Understanding their retention strategies and perceived effectiveness can provide insights into audio-based content's impact on audience retention. The "Other" category (16.39%) encompasses a variety of content types. Creators in this category may employ diverse strategies based on their specific niches. Exploring their approaches and perceived effectiveness in audience retention can help uncover unique and creative strategies that content creators use to retain their viewers. 4.3.7 Average Length of YouTube Videos The distribution of the average length of YouTube videos created by content creators is presented in Figure 9 below. Figure 9 Average Length of Video To examine the relationship between audience retention strategies and content creators' perceived effectiveness, we can analyze the provided data on the distribution of YouTube video lengths. The data categorizes videos into different length ranges: 0-59 seconds, 1-10 minutes, 11-20 minutes, 21-30 minutes, and 31 minutes or more (Geissler et al., 2021). The choice of video length is a critical aspect of content creation strategy, as it can significantly impact audience retention and how content creators perceive their effectiveness. Content creators must consider their target audience's preferences, the nature of their content, and the platform's algorithms when deciding on video length. The data reveals that the most significant proportion of content creators (32.67%) produce videos within the 1-10 minute range. This suggests that many content creators recognize the importance of concise and engaging content. Shorter videos in this range may have higher audience retention rates as they cater to viewers with limited time or shorter attention spans. On the other hand, 24.75% of content creators produce videos in the 11-20 minute range. These creators may focus on more in-depth or tutorial-style content, which could require a longer duration to convey valuable information effectively. However, maintaining audience engagement over longer videos can be challenging, making it crucial for these creators to employ effective retention strategies (Geissler et al., 2021)). It is also interesting to note that 7.43% of content creators produce videos that are 31 minutes or longer. These longer videos may indicate a commitment to in-depth storytelling or educational content but pose higher retention challenges. Therefore, understanding how creators in this category perceive their effectiveness in retaining audiences and whether they employ specific strategies for longer content can provide valuable insights. In summary, the data on video length distribution provides an initial glimpse into content creators' strategies related to audience retention and perceived effectiveness. Further analysis, including surveys or interviews with content creators, could help explore the specific retention strategies employed across different video-length categories and how these strategies impact creators' perceptions of their effectiveness in engaging and retaining their audience. 4.3.8 YouTube Search Engine Optimization Perception Table 23 YouTube Search Engine Optimization Perception Table 24 The results of the Two-Factor ANOVA analysis The results of the Two-Factor ANOVA analysis for YouTube Search Engine Optimization (SEO) perception reveal fascinating insights into how content creators perceive the effectiveness of their SEO strategies. The p-value for the Rows factor, representing different categories or groups, is exceptionally low at 4.37E-06 (Ma et al., 2023). This signifies a significant variation in how content creators perceive SEO effectiveness across these categories. In essence, the content creators' perceptions of SEO effectiveness are not uniform; there are substantial differences in perception depending on the specific category. Similarly, the p-value for the Columns factor, representing different SEO strategies or practices, is astonishingly low at 1.507E-155. This points to a highly significant difference in perception based on the choice of SEO strategy. It implies that content creators' perceptions of SEO effectiveness are heavily influenced by their specific SEO approach. Content creators should take heed of the statistical significance of SEO strategy choice. The data suggests that their specific SEO practices can significantly impact their perception of effectiveness. This highlights the need for content creators to be strategic and intentional when selecting their SEO approaches. It is not just about optimizing content for search engines but also about choosing the strategies that align best with their content type, target audience, and overall goals. For instance, if a content creator is in the "Rows" category with a statistically significant impact on perception, they may want to explore the practices that yield the highest perceived effectiveness (Lemay & Doleck, 2022). Additionally, these results underscore the importance of ongoing SEO evaluation and adaptation. Content creators should not be wedded to a single SEO strategy but should be open to experimenting with different approaches. Regularly monitoring the performance and perception of these strategies can lead to more informed decisions and continuous improvement. In essence, the statistical findings here reinforce the dynamic nature of SEO in the content creation landscape. In conclusion, the Two-Factor ANOVA analysis emphasizes that content creators' perceptions of SEO effectiveness are not uniform and are heavily influenced by the categories and strategies they engage with. These findings encourage content creators to be strategic and adaptive in their SEO practices, choosing approaches that align with their content and goals while remaining open to experimentation and improvement. By doing so, content creators can optimize their SEO efforts and enhance their visibility and reach on YouTube. 4.3.9 Response levels about YouTube Search Engine Optimization Figure 10 Sum of Total by Agree Analyzing the data, it is evident that respondents' opinions vary across different aspects. For example, in the case of "Subscriber Engagement," a significant portion (67.3%) of respondents either "Agree" or "Strongly Agree," indicating that they believe in the importance of engaging with subscribers. On the other hand, in the variable "Devious SEO," the responses are more evenly distributed, with 35.6% "Agree" and 29.2% "Disagree." This suggests that opinions on the use of devious SEO tactics are more divided among respondents (Ma et al., 2023). Moreover, you can identify trends in respondents' perceptions. For instance, variables related to video titles, such as "Appealing Video Title" and "Relevant Video Title," show that a considerable percentage of respondents either "Agree" or "Strongly Agree," indicating the significance of well-crafted video titles. Additionally, variables like "Profit Awareness" and "SEO Effectiveness" have notable proportions of respondents who "Strongly Agree" or "Agree," suggesting that these factors are considered highly important in content creation. In summary, this data analysis reveals insights into respondents' perceptions and opinions regarding various aspects of content creation and YouTube strategies. It demonstrates that opinions vary across different variables, and some factors are considered more important or significant by a majority of respondents. These insights can be valuable for content creators and YouTube strategists to understand the perspectives of their audience and make informed decisions in their content creation and optimization efforts. 5. CONCLUSIONS AND RECOMMENDATIONS 5.1 Purpose of the Study One primary purpose of this study is to examine the factors that influence content creators' perceived effectiveness. By exploring the relationship between audience retention strategies and content creators' perceived effectiveness, the study aims to uncover the key strategies and practices contributing to a creator's success. This knowledge is beneficial for content creators seeking to enhance their performance and YouTube as it seeks to optimize its algorithms and support its creator community effectively. Additionally, the study aims to identify the most valuable tools YouTubers utilize. In the ever-evolving landscape of content creation, the tools and resources creators use can significantly impact the quality and reach of their content. By identifying these tools, the study can assist aspiring and experienced content creators in optimizing their workflows, potentially fostering more engaging and valuable content on the platform. Furthermore, the research intends to explore the barriers preventing YouTube video uploads. Understanding these barriers is crucial for YouTube and content creators alike. For the platform, addressing these obstacles can improve user experience and content diversity. For creators, identifying common challenges and potential solutions can enhance their content creation journeys and reduce frustration (Lemay & Doleck, 2022). The study seeks to analyze content types provided on YouTube channels. YouTube hosts a vast array of content, and categorizing these types can offer insights into content trends and audience preferences. Such knowledge can be valuable for content creators and YouTube, as it aids in content discovery, audience targeting, and platform optimization. The purpose of this study is multifaceted, encompassing the exploration of audience retention strategies, identification of useful tools, examination of barriers, and analysis of content types on YouTube. These endeavors collectively contribute to a more comprehensive understanding of content creation on the platform, offering practical insights and recommendations for content creators and stakeholders in the YouTube ecosystem. 5.2 Procedures Conducting a research study on YouTube content creators and their audience retention strategies involves a well-defined procedure. In the initial phase, it is imperative to establish clear research objectives (Lemay & Doleck, 2022). The primary goal of this study is to gain a comprehensive understanding of how content creators employ audience retention strategies and how they perceive the effectiveness of these strategies. These objectives serve as the foundation for crafting the research methodology. The next critical step is identifying the target population. In this case, the focus is on YouTube content creators and their viewers, as these groups play pivotal roles in shaping audience engagement. Sampling methods should be carefully chosen to ensure the selected participants represent the broader YouTube content creator and viewer community. Random sampling may be employed to minimize bias and enhance the generalizability of the findings. Developing a structured questionnaire is another crucial element of the procedure. The questionnaire should cover critical areas such as audience retention strategies, content creator demographics, and their perceived effectiveness. Pretesting the questionnaire is essential to iron out any ambiguities or issues related to question wording and order. Participant recruitment involves reaching out to YouTube content creators, possibly through their YouTube channels or via email, if access to their contact information is available. Data collection can be facilitated through online survey tools while safeguarding data privacy and confidentiality. Once the data is gathered, thorough statistical software analysis is necessary to explore relationships between audience retention strategies and content creators' perceived effectiveness. The interpretation of results will provide insights into whether there is a significant correlation between these variables, shedding light on effective audience engagement practices within the YouTube community (Ma et al., 2023). Finally, a comprehensive research report, adhering to ethical considerations, will be prepared for potential publication in academic journals or dissemination within the YouTube community to benefit content creators and viewers alike. This systematic procedure ensures that the study is conducted rigorously, providing valuable insights into the dynamic world of YouTube content creation. 5.3 Summary of Major Findings 5.3.1 Objective One Content creators aged 18-24 years (26.24%) constitute a substantial portion of the study, often characterized by their familiarity with digital trends and technology. Those aged 25-34 years (27.72%) represent a balance between creativity and expertise, bridging generational gaps in content creation. The study revealed a substantial gender gap within the content creator community, with males accounting for 47.52% and females for 36.14%. Understanding strategies employed by male and female content creators is essential for gender-specific approaches to content creation and audience retention. A majority of survey participants (66.83%) identified themselves as content creators, emphasizing the prevalence of content creators within the sample. Understanding the practices and strategies of content creators provides valuable insights into audience retention tactics. The remaining 33.17% who do not identify as content creators offer insights into factors deterring individuals from content creation, contributing to a comprehensive understanding of audience retention. A majority of survey participants (66.83%) identified themselves as content creators, emphasizing the prevalence of content creators within the sample. Understanding the practices and strategies of content creators provides valuable insights into audience retention tactics. The remaining 33.17% who do not identify as content creators offer insights into factors deterring individuals from content creation, contributing to a comprehensive understanding of audience retention. Content creators' experience levels vary, with 39.60% having less than a year of experience, 42.57% having 1-5 years, 12.87% having 6-10 years, and 4.95% having 11 years or more. 5.3.2 Objective Two Majority of content creators (26.24%) are aged 18-24, while 27.72% are aged 25-34. Content creators aged 35-44 constitute 25.74% of the sample. Participation decreases in older age groups, with no participants aged 65 or older. Distinction between content creators and non-creators is crucial for tailored tool recommendations. Understanding ownership status helps differentiate content creators from viewers. Common content types include providing solutions (20.56%) and lecturing explanation (23.06%). Vlogs (19.17%) and audio podcasts (9.17%) are prevalent. Content type diversity requires specific tool recommendations. Common barriers include low views (20.23%) and low interaction (20.92%). Poor performance (13.33%) and content quality (13.79%) are challenges. Technical equipment (12.41%) and internet issues (11.95%) affect content creators. Understanding barriers helps identify tools to overcome challenges. Websites (16.99%) and trends content (15.34%) are utilized. Diversity in resource usage requires tailored tool recommendations. 5.3.3 Objective Three Content creators' ages have a statistically significant impact on how they perceive the effectiveness of audience retention strategies. Younger creators may prioritize different strategies than older ones. While marginally significant, gender differences can influence content creators' perceived effectiveness in audience retention strategies, suggesting subtle variations in approaches (Geissler et al., 2021). Self-identification as a content creator has a marginal influence on perceived effectiveness, indicating slight differences in perspectives among content creators and non-creators. Owning or not owning a YouTube channel does not significantly affect content creators' perceived effectiveness in audience retention strategies. The length of experience in content creation has a statistically significant impact on perceived effectiveness, with more experienced creators potentially having a more positive outlook on their strategies. Content creators produce videos of varying lengths, with the majority opting for 1-10-minute videos. Video length choice can impact audience retention and effectiveness perceptions. Content creators' perceptions of the effectiveness of different YouTube SEO strategies vary significantly. The choice of SEO practices can strongly influence their perceptions. A majority of content creators agree on the importance of engaging with subscribers as an audience retention strategy. Well-crafted and relevant video titles are widely considered important by content creators for audience retention. Content creators generally recognize the importance of profit awareness and SEO effectiveness as factors influencing their audience retention strategies. 5.4 Conclusions 5.4.1 Objective 1 - Audience Retention Strategies Content creators on YouTube employ a diverse range of strategies to enhance audience retention. These strategies vary based on factors such as age, gender, content creator status, and content type. Younger creators tend to focus on visually appealing, fast-paced content, while older creators may employ traditional storytelling techniques (Geissler et al., 2023). Gender can subtly influence strategies, and content creators who self-identify as such may have slightly different approaches. However, owning a YouTube channel does not significantly impact strategy perception. Experienced creators may perceive their strategies as more effective. Video length choices and YouTube SEO practices also play a role in retention strategy effectiveness. 5.4.2 Objective 2 - Barriers to Content Creation Content creators need help uploading videos on YouTube, including low views, low interaction, poor performance, content quality, technical equipment, and internet issues (Holliman & Rowley, 2014). These barriers can hinder audience retention efforts. Strategies to overcome these barriers include optimizing video elements, encouraging viewer interaction, improving content quality, and investing in better equipment. 5.4.3 Objective 3 - Relationship with Perceived Effectiveness Content creators' demographic characteristics, such as age, gender, content creator status, and experience, have varying degrees of influence on their perceived effectiveness in audience retention strategies (Holliman & Rowley, 2014). Video length choices and YouTube SEO practices significantly affect how creators perceive the effectiveness of their strategies. 5.5 Implications 5.5.1 Audience Segmentation Content creators should recognize the importance of segmenting their audience based on age, as different age groups may respond better to specific retention strategies. Similarly, understanding gender-related nuances can help tailor content to diverse audiences. 5.5.2 Content Quality Improving content quality is crucial, as it directly impacts audience retention. Creators should invest in better equipment, enhance production values, and create engaging visuals to retain viewers. 5.5.3 Engagement Emphasis Encouraging viewer interaction and engagement should be a central strategy for content creators. This includes responding to comments, creating a sense of community, and using call-to-action prompts effectively. 5.5.4 SEO Strategy The choice of SEO practices significantly influences perceived effectiveness. Creators should regularly evaluate and adapt their SEO strategies to align with their content, audience, and goals. 5.6 Recommendations 5.6.1 Targeted Strategies Tailor audience retention strategies based on the age and gender of the target audience. Younger audiences may prefer visually engaging content, while older audiences may respond better to storytelling. 5.6.2 Continuous Improvement Content creators should consistently work on enhancing content quality, as this is a fundamental factor in audience retention. Regularly upgrading equipment and production values can make a significant difference. 5.6.3 Interactive Content Incorporate interactive elements within videos to boost viewer engagement. Use call-to-action prompts, respond to comments, and foster a sense of community among viewers. 5.6.4 SEO Optimization Invest time in understanding and optimizing YouTube SEO practices. Experiment with different approaches and monitor their impact on audience retention. 5.6.5 Overcoming Barriers To improve audience retention, content creators should address common barriers such as low views and interaction by optimizing video elements, improving content quality, and encouraging engagement. 5.6.6 Content Length Consider the target audience and content type when deciding on video length. Shorter videos may retain viewers with shorter attention spans, while longer videos can cater to more in-depth content needs. 5.6.7 Demographic Awareness Creators should be aware of their own demographic characteristics and how they may influence their perceptions of strategy effectiveness. Awareness can lead to more informed decisions. Appendix A: Consent Form Appendix B: IRB Approval Letter Appendix C: The Questionnaire Part A: Demographic Questions Please proceed if you have read the above consent form and you do not have any further questions regarding the study. Are you a content creator? Yes No Do you have a YouTube channel? Yes No Please choose the correct category for your current age? 18-24 years 25-34 years 35-44 years 45-54 years 55-64 years 65 years or older Please choose the correct gender category for you? Male Female Prefer not to say Other ………… How often do You Publish contents into your channel? …………………………………………………………………………………………………………………………………………………………………………………………………… Please How long have you been working in content creation of your service in content creation: Less than a year 1-5 years 6-10 years 11 years or more Please select what content do you provide to your YouTube channel YouTube content? (Check all that apply). Provide solutions Lecturing Explanation Making vlogs Audio Podcasts Showing Movies Other …………………………… What barriers have prevented you from uploading your YouTube videos to you channel are the barriers that prevent you from creating YouTube on to your videos into your channel: (Check all that apply). Low views Low interaction Internet weakness Poor performance Content quality Technical equipment Other ………………………… What are the resources that you use to support your YouTube content? are used to support your YouTube content: Check all that apply Websites Social Media Family and Friends Trends Contents Others ……………………… What is the average length of your YouTube videos: 1 – 10 minutes 11 – 20 minutes 21 – 30 minutes 31 minutes – or more Part B: YouTube Search Engine Optimization: Please, indicate your level of agreement with the statements below related to your usage perception of YouTube SEO (Funk, 2021). We thank you for your time spent taking this survey. Your response has been recorded. References Abt, K. (1987). Descriptive data analysis: a concept between confirmatory and exploratory data analysis. Methods of information in medicine, 26(02), 77-88. Acharya, A. S., Prakash, A., Saxena, P., & Nigam, A. (2013). 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READINESS FOR LIFELONG LEARNING OF VOLUNTEERS AFFILIATED WITH A 4-H YOUTH DEVELOPMENT PROGRAM IN THE SOUTHERN REGION OF THE UNITED STATES A Dissertation Submitted to the Graduate Faculty of the Louisiana State University and Agricultural and Mechanical College in partial fulfillment of the requirements for the Degree of Doctor of Philosophy in The School of Human Resource Education and Workforce Development by Kenneth Kimani Kungu B.A., Egerton University, 2001 M.S., Louisiana State University, 2005 May, 2010 ii © Copyright 2010 Kenneth Kimani Kungu All Rights Reserved iii ACKNOWLEDGEMENTS As I ponder about my journey through graduate school, I feel greatly indebted to many people who in one way or another sacrificed and invested in my academic pursuits. As I reflect on the various forms of support I have been fortunate to receive, I see behind them people who sacrificed and went out of their way to help me achieve my goals, even when they were under no obligation to do so. I am as humbled as I am grateful. It may not be possible to list all the people to whom I owe gratitude here but I do recognize and sincerely appreciate them all. May God bless you all. I would like to register my deepest gratitude to Dr. Krisanna Machtmes, my major professor, for the intellectual and emotional support she has accorded me consistently since I joined LSU. Dr. Machtmes, thank you for the time that you so generously gave, and your constant words of encouragement that I so needed as I struggled to shape the ideas for this dissertation. I have benefited from your friendship, insightful counsel, empathy, affability, generosity, inspiration, and incredible patience. You demonstrated both a steadfast commitment to my academic success as well as a deep concern for my general well being. I consider myself very fortunate to have had you for an advisor. I am also very grateful to your husband Roland and son Ryan for their friendship and generosity. I would not have been able to do this without the tremendous support both in terms of financing my study and program-related advice that I received from Dr. Michael F. Burnett, Professor and Director, SHREWD. Dr. Burnett, you welcomed me so warmly to LSU and since then you have always looked out for my best interests. Despite running a really busy schedule, you have made time to address my concerns and many a times gone out of your way to ensure I am comfortable. I don’t take it for granted. I am always humbled and inspired at the same time iv by your actions and gestures. I am also grateful for your intellectual presence both in my graduate work and my dissertation. I owe a great deal of gratitude to my committee members Dr. Gerri Johnson, Dr. Janet Fox and the dean’s representative Dr. Denise Egea-Kuehne. Thank you for reading my work and for offering insightful comments that have helped shape my dissertation. I have visited with each one of you and you have shown me nothing but kindness and support. I have benefited from your counsel, direction, time, resourcefulness, and incredible patience and understanding. I have learned a lot from each one of you and I consider myself very fortunate to have had each of you serving on my committee. I would also like to register my gratitude to Dr. Satish Verma. I have been fortunate enough to benefit from his advice, encouragement and support. He not only lent me a listening ear, but also provided resources to facilitate travel to conferences. To my friends, too many to name here, thank you for being there for me. I appreciate each and every one of you. Special thanks to Dr. Mugambi, Dr. Wachanga, Dr. Ngunjiri, Dr. Carpenter, Kimani, Okech, Wairimu, Mimba, Erastus, Syanda, and Becky. To my family, who have always placed within my reach a never-drying well of love, acceptance, support and encouragement to drink from, and who always exhibit unparalleled patience even in the face of perennial whining, I will eternally be grateful. To my parents, Patrick and Alice Kimani, words fail me every time I try to register on paper my appreciation for who you are and the role you’ve played in my life. You are an embodiment of unconditional love, sacrifice, generosity, humility and faith. Thank you for your resolute support for and investment in my goals. To my siblings, Waithera, Wanjiru, Gitau and Mburu, thank you for your friendship and for always being there for me. Where would I be without your encouraging v words and unwavering support? Uncle Ben and Aunty Joyce, James Maina, and all other family members, thank you for your support. vi TABLE OF CONTENTS ACKNOWLEDGEMENTS………………………………………...……………. iii LIST OF TABLES………………………………………………….…………….viii LIST OF FIGURES…………………..…………………………….……………. xii ABSTRACT………………………………….…………………….……………. xiii CHAPTER 1. INTRODUCTION………………………………………………………….1 Rationale………………………………...…………………………………..1 Problem Statement……………………………………………………..……9 Purpose of the Study…….…………………………………………………11 Research Objectives………….…………………………………………….12 Significance………………………………………….……………………..13 Definition of Terms…………………………………….…………………..14 2. REVIEW OF RELATED LITERATURE………………………………...15 Learning Defined………………………………………………………….15 Lifelong Learning Defined……………………………………………….. 16 What Counts as Lifelong Learning………………………………………...18 The Case for Lifelong Learning……………….…………………………..19 A Brief History of Lifelong Learning…………………….………………..21 Lifelong Learning Policy……………………………………….………….22 Lifelong Learning Empirical Studies……………………………….….….25 Readiness to Respond to Triggers for Learning………………….….…….32 Self-Directed Learning and Lifelong Learning……………………..……..47 Self-Directed Learning Defined……………………………………..…....48 Prevalence of Self-Directed Learning………………………………..…....49 Self-Directed Learning Models………………………………….……..….50 Self-Directed Learning Readiness……………………………….……..….57 Readiness to overcome Deterrents to Participation……………….…..…...65 Summary………………………………………………………….………..78 3. METHODOLOGY…………………………………………….………….80 Population and Sample………………………………………….…………80 Ethical Considerations and Study Approval………………………………80 Instrumentation……………………………………………………….…...80 Data Collection……………………………………………………….……86 Data Analysis………………………………………………………….…...88 4. RESULTS ……………………………………………………….………..92 Objective One ……….…………………………………………………….92 Objective Two.…………………………………………………………......99 Objective Three…………………………………………….……………..119 vii Objective Four...…………………………………………………….……130 5. CONCLUSIONS AND RECOMMENDATIONS……………………...135 Purpose of the Study……………………………………………………..135 Procedures………………………………………………………………..136 Summary of Major Findings……………………………………………..137 Conclusions, Implications and Recommendations...…………………….143 REFERENCES……………………………………………………………….... 149 APPENDIX A. LOUISIANA STATE UNIVERSITY INSTITUTIONAL REVIEW BOARD (IRB) FOR PROTECTION OF HUMAN SUBJECTS APPROVAL LETTER...……………………………………..………..…159 B. READINESS FOR LIFELONG LEARNING SURVEY INSTRUMENT………………………………..……………………........161 C. SURVEY PRE-NOTICE ……………………..………………………….168 D. SURVEY FIRST LETTER …………………………………….…..…….170 E. SURVEY FIRST REMINDER ……………….………………..…….….172 F. SURVEY SUBSEQUENT REMINDERS ……….…………….…….….174 G. PERMISSION TO USE SDLRS QUESTIONNAIRE …………...….…..176 VITA ……………………………………………………………………..………178 viii LIST OF TABLES 1. Summary of Factors Identified in Studies of Learning Orientations……..…67 2. Perceived barriers to learning as identified by Carp, Peterson, and Roelfs (1974) and categorized by Cross (1981) ………………………..69 3. Age Distribution of Adult Volunteers Affiliated with a 4-H Youth Development Program in the Southern Region of the United States……….93 4. Self-Identified Ethnicity of Adult Volunteers Affiliated with a 4-H Youth Development Program in the Southern Region of the United States ………………………………………………………………..……....94 5. Highest Level of Education Completed by Adult Volunteers Affiliated with a 4-H Youth Development Program in the Southern Region of the United States………………………………………………………………..94 6. Yearly Net Incomes as Reported by Adult Volunteers Affiliated with a 4-H Youth Development Program in the Southern Region of the the United States …………………………………………………………..95 7. Marital Status Reported by Adult Volunteers Affiliated with a 4-H Youth Development Program in the Southern Region of the United States………………………………………………………………………..95 8. Current Employment Status as Reported by Adult Volunteers Affiliated with a 4-H Youth Development Program in the Southern Region of the United States…………………………………………………………….96 9. Length in Current Employment Reported by Adult Volunteers Affiliated with a 4-H Youth Development Program in the Southern Region of the United States ……………………………………………………………97 10. Current Occupational Categories of Adult Volunteers Affiliated with a 4-H Youth Development Program in the Southern Region of the United States ……………………………………………………………97 11. Number of Times Adult Volunteers Affiliated with a 4-H Youth Development Program in the Southern Region of the United States Changed Jobs in the Last Five Years ………………………………………98 12. Length of Time the Adult Volunteers Report Having Volunteered with the 4-H Youth Development Program in the Southern Region of the United States ………………………………………………………...98 13. Preference for a Format for Learning Expressed by Adult Volunteers ix Affiliated with the 4-H Youth Development Program in the Southern Region of the United States……………………………..……………………99 14. Description of the Level of Agreement of Adult Volunteers Affiliated with the 4-H Youth Development Program in the Southern Region of the United States with Statements Reflecting Self-Directed Learning Readiness Characteristics……………………………………………………..101 15. Factor Loading, Eigenvalues, and Variance for Items Representing Self-Directed Learning Readiness for a Rotated Four-Factor Solution ……...103 16. Description of the Likelihood that Adult Volunteers Affiliated with the 4-H Youth Development Program in the Southern Region of the United States Would Seek and Participate in Learning when Faced with Triggers for Learning………………………………………………………...106 17. Factor Loading, Eigenvalues, and Variance for Items Representing Readiness to Respond to Triggers for Learning for a Rotated Three-Factor Solution ……………………………………………………….110 18. Description of the Level of Agreement of Adult Volunteers Affiliated with the 4-H Youth Development Program in the Southern Region of the United States with Statements Reflecting the Readiness to Overcome Deterrents to Participation in Learning …… ……………………113 19. Factor Loading, Eigenvalues, and Variance for Items Representing Readiness to Overcome Deterrents to Participation in Learning for a Rotated Four-Factor Solution ……………………………………………..115 20. Group Sizes, Overall Readiness for Lifelong Learning Item Mean Scores, and Standard Deviation by Gender for Volunteers Affiliated with a 4-H Youth Development Program in the Southern Region of the United States …………………………………………………..………..119 21. One Way Analysis of Variance Illustrating Differences in Overall Readiness for Lifelong Learning by Gender for Volunteers Affiliated with a 4-H Youth Development Program in the Southern Region of the United States …………………………………………………………....120 22. Group Sizes, Overall Readiness for Lifelong Learning Item Mean Scores, and Standard Deviation by Ethnicity for Volunteers Affiliated with a 4-H Youth Development Program in the Southern Region of the United States …………………………………………………..………..120 23. One Way Analysis of Variance Illustrating Differences in Overall Readiness for Lifelong Learning by Ethnicity for Volunteers Affiliated x with a 4-H Youth Development Program in the Southern Region of the United States ……………………………………………………………121 24. Group Sizes, Overall Readiness for Lifelong Learning Item Mean Scores, and Standard Deviation by Highest Level of Education Completed by Volunteers Affiliated with a 4-H Youth Development Program in the Southern Region of the United States. .…………..………..122 25. One Way Analysis of Variance Illustrating Differences in Overall Readiness for Lifelong Learning by the Highest Level of Education Completed for Volunteers Affiliated with a 4-H Youth Development Program in the Southern Region of the United States ……………………..122 26. Group Sizes, Overall Readiness for Lifelong Learning Item Mean Scores, and Standard Deviation by Yearly Net Income for Volunteers Affiliated ith a 4-H Youth Development Program in the Southern Region of the United States. ………………………...…………..………..123 27. Group Sizes, Overall Readiness for Lifelong Learning Item Mean Scores, and Standard Deviation by Marital Status for Volunteers Affiliated with a 4-H Youth Development Program in the Southern Region of the United States. ………………………...…………..……….124 28. One Way Analysis of Variance Illustrating Differences in Overall Readiness for Lifelong Learning by Marital Status for Volunteers Affiliated with a 4-H Youth Development Program in the Southern Region of the United States …….………………………………………...124 29. Group Sizes, Overall Readiness for Lifelong Learning Item Mean Scores, and Standard Deviation by Presence of Children at Home for Volunteers Affiliated with a 4-H Youth Development Program in the Southern Region of the United States. .…………..………………..125 30. One Way Analysis of Variance Illustrating Differences in Overall Readiness for Lifelong Learning by the Presence of Children at Home for Volunteers Affiliated with a 4-H Youth Development Program in the Southern Region of the United States …………………………….125 31. Group Sizes, Overall Readiness for Lifelong Learning Item Mean Scores, and Standard Deviation by Employment Status for Volunteers Affiliated with a 4-H Youth Development Program in the Southern Region of the United States…………………………...…………..……..126 32. One Way Analysis of Variance Illustrating Differences in Overall Readiness for Lifelong Learning by Employment Status for Volunteers Affiliated with a 4-H Youth Development Program in the Southern Region of the United States …….…………………………………….…..127 xi 33. Group Sizes, Overall Readiness for Lifelong Learning Item Mean Scores, and Standard Deviation by Current Occupational Category for Volunteers Affiliated with a 4-H Youth Development Program in the Southern Region of the United States. .…………..….……127 34. One Way Analysis of Variance Illustrating Differences in Overall Readiness for Lifelong Learning by Current Occupational Category for Volunteers Affiliated with a 4-H Youth Development Program in the Southern Region of the United States ……………….……128 35. Group Sizes, Overall Readiness for Lifelong Learning Item Mean Scores, and Standard Deviation Based on Whether Current Employment Reported by Volunteers Affiliated with a 4-H Youth Development Program in the Southern Region of the United States Required Continuous Certification/Licensure ……………………………………….128 36. One Way Analysis of Variance Illustrating Differences in Overall Readiness for Lifelong Learning Based on Whether Current Employment Reported by Volunteers Affiliated with a 4-H Youth Development Program in the Southern Region of the United States Required Continuous Certification/Licensure ……………………………………….129 37. Group Sizes, Overall Readiness for Lifelong Learning Item Mean Scores, and Standard Deviation by Preferred Format for Learning for Volunteers Affiliated with a 4-H Youth Development Program in the Southern Region of the United States. .…………..…………………129 38. One Way Analysis of Variance Illustrating Differences in Overall Readiness for Lifelong Learning by Preferred Format for Learning for Volunteers Affiliated with a 4-H Youth Development Program in the Southern Region of the United States ……………………………….130 39. Significance of the Regression Equation and Model Summary Employing Four Independent Variables in Predicting Overall Readiness for Lifelong Learning of Volunteers Affiliated with a 4-H Youth Development Program in the Southern Region of the United States ………………………………...133 40. Coefficient Values, Standard Errors, Standardized Coefficient Values, T Values, and Significance Levels for Independent Variables Retained in the Regression Equation Predicting Overall Readiness for Lifelong Learning Score ……………………………………………………………….134 41. Excluded Variables, Standardized Coefficients, T Values, Significance Levels, and Partial Correlations for the Regression Equation Predicting Overall Readiness for Lifelong Learning Score ……………………………..134 xii LIST OF FIGURES 1. Self-Directed Learning Readiness Four-Factor Solution Scree Plot……..…104 2. Readiness to Respond to Triggers for Learning Three-Factor Solution Scree Plot ……………………………………………………………………111 3. Readiness to Overcome Deterrents to Participation in Learning Four-Factor Solution Scree Plot…………………………………….………116 4. Histogram Depicting Standardized Residuals for the Dependent Variable Overall Mean………………………………………………………………..132 xiii ABSTRACT The purpose of this study was to explore and determine the level of readiness for lifelong learning of volunteers affiliated with a 4-H youth development program in the southern United States. Based on a literature review, readiness for lifelong learning was conceptualized as incorporating aspects of response to triggers for learning, self-directed learning readiness, and a readiness to overcome deterrents to participation in learning. The Readiness for Lifelong Learning Survey, a 75 item Likert-type scale, was developed and administered online to 1815 adult volunteers who had provided usable emails in a enrollment database system. The final response count was 277 representing a 15.3% response rate. The overall readiness for lifelong learning score fell within the “high readiness” category on an interpretive scale developed by the researcher. There were significant differences in the overall readiness for lifelong learning mean score based on marital status, yearly net income and preferred format for learning. No significant differences in readiness for lifelong learning mean score was observed based on gender, ethnicity, and highest level of education completed, presence of children at home, employment status, and occupational category, and whether current employment requires continuous certification. A regression model with four demographic variables found that explained a significant portion of the variance in the overall readiness for lifelong learning score. Preference for “web-based/online training” and “divorced” marital status increased the overall readiness for lifelong learning score, while earning “more than $100,000” in yearly net income and being “single never married” reduced the overall readiness for lifelong learning score. 1 CHAPTER 1 INTRODUCTION Levin (1998) describes a system of lifelong learning as one in which both formal and informal learning opportunities are available to individuals to choose from in their circumstances, to help in meeting their societal and personal needs over the entire life cycle. Cross and Hilton (1983) view lifelong learning as expressing an ideal where people would, throughout their lives, be able to move easily in and out of learning opportunities acquiring knowledge, skills and attitudes necessary for independent living in a complex society. Learning is undertaken for meeting both occupational and personal needs. Learning opportunities are broadly conceived to include a variety of ways in which people engage in learning such as enrollment full-time or part-time in a college or vocational school, attending seminars and workshops within the community, training at places of employment, television courses, independent reading/study projects (library or online), correspondence courses, mentoring, and goal-directed informal learning from colleague or friend among other ways. Lifelong learning as a concept is not new. The concept of learning throughout life is mentioned in ancient writings (Cropley, 1980). However, the rapid and pervasive nature of change due to globalization and technological developments witnessed from the 1960’s brought debates on lifelong learning to the center stage (Field, 2000a). Rationale It is widely accepted that society is grappling with constant change occasioned by globalization and rapid technological development. According to Desjardins, Rubenson, and Milana (2006), countries are undergoing fundamental economic transformations in which knowledge and information are becoming the foundations of economic activity. The new global economy comes with a new set of transitions and adjustment challenges at the societal, industry 2 and individual levels. Lifelong learning is viewed as the vehicle which enables people to adapt and meet the challenges of the new economy. According to Houle (1973), lifelong learning is no longer just desirable, but necessary. He adds that opportunities for continuing learning should be made available to adults throughout their lives. According to Levin (1998), past conceptions of education involved the assumption that adult roles remained relatively stable over a typical lifetime. Thus preparation for occupational, family and civic roles could largely be accomplished in the early education years before entering adult roles. This idea is what Tight (1988b) calls “front-end” education, where education is essentially confined to the earlier years of life in preparation for the rest of adult life. This conception has been challenged by many writers including Houle (1973) and more recently Field (2000a) who view continuous education throughout life as a necessary component of career, civic and private life owing to the constantly changing conditions of modern life. Dramatic economic and technological changes and innovations which have affected virtually every facet of human life (Field, 2000a) create the forces that make continuous engagement with learning throughout life a necessity. Field (2000a) offers at least three forces that are driving the agenda for lifelong learning. The first is the increased economic and social importance of knowledge, where scientific, technological and other information are so significant to the success of any nation. In what is called a knowledge society, the unskilled/uneducated face diminishing opportunities for themselves and also drag back the application of knowledge with implications for the larger society. The second is the tremendous impact of new information and communication technologies (ICTs) with applications which have transformed not just private life, but also how industry and services operate. “Governments and corporations are fearful that innovations arising from new applications of the new technologies will leave them stranded while competitors race 3 ahead” (Field, 2000a, p. 17). The third force is globalization which includes cross-border corporate expansion and intensifying global competition. Globalizing tendencies have had impact in the economic, cultural and social spheres. Lifelong learning is seen as a way of dealing with globalizing tendencies. To become global citizens people are being forced to acquire new skills which include linguistic, interpersonal, cultural and technological. Export of production is slowly making competition for jobs a global affair, with jobs moving to areas which have better qualified and/or cheaper labor force. Lifelong learning is widely regarded as a defense against these forces which are experienced at, and have implications for the economic/societal, organizational and individual levels of society. Highlighted below are at least four considerations that make the case on why lifelong learning is important. Economic Considerations According to the Organization for Economic Co-operation and Development (OECD, 2004), there is a positive relationship between educational attainment and economic growth. Human capital development is considered to be at the center of countries’ economic competitiveness. Politicians are acknowledging that “knowledge is the most important source of future advantage” for their countries (Field, 2000a, p. 1). Nijhof (2005) asserts that the more knowledge-intensive the economy becomes, the more profit, efficiency and innovation will occur. According to Field (2000a), there is a need not just to develop a small minority of skilled workers or specialized professionals, but the whole workforce. A nation’s economic competitiveness is pegged on the development of a more productive and efficient workforce. Lifelong learning is being seen as central to developing a country’s economic competitiveness. According to Ellinger (2004), learning is seen as the source of competitive advantage for organizations. Skills and competence of the workforce are a major factor in the economic 4 performance and success at the organization level (OECD, 2004). There is an economic necessity for constant improvement of skills in the workplace. Many organizations invest in the continuing education of their employees, as it is seen as the only way to stay competitive. The professions are also recognizing the importance of developing practitioners to be lifelong learners (Merriam, Caffarella, & Baumgartner, 2007). At the individual level, economic imperatives have to do with remaining employable in the face of a less stable labor market. Overall, the concept of a career for life is slowly diminishing. According to the OECD (2004), individuals are experiencing more frequent changes in jobs over their working life. Fischer (1999) notes that most people change careers several times in their lives, even though their schooling was designed to prepare them for their first career. Also the pace of technological change is so fast that skills to use technology become obsolete in 5-10 years. The inability to handle new technologies is also putting many people’s future at risk (Kathryn, 1999). According to Levin (1998), firms are increasingly looking for contract workers due to the instability in product markets and rising costs of benefits for employees. Redundancies or closures are also rendering individuals jobless (Field, 2000a). There is no doubt that the employment market has become relatively unpredictable. Unemployment is a more permanent feature that requires constant retraining, job development and new occupational placements (Levin, 1998). Since occupations have become less stable and less predictable (Field, 2000a), learners are being encouraged to assume more responsibility for their own learning and development to remain employable (Ellinger, 2004). Engagement in lifelong learning is a way to help individuals become flexible and adaptable, providing opportunities to learn new skills as needed to remain employable (Levin, 1998). 5 Complexity of Social Life Away from the economic front and rooted in the daily lives of individuals are found a series of intimate and often small scale demands for change and adaptability (Field, 2000a). Individuals turn to learning to help adapt to changes in the wider context of individual values, social relationships, and living patterns. According to Levin (1998), increasing complexities of family and civic life require constant re-learning. Some of the complexities arise from some factors discussed by Field (2000a), a few of which include: marriage no longer being a once-for-all, linear stage; the family may be made up of children from several different pairs; there’s less certainty in decision making even for those who retain a single partner and conventional career; social networks are now more open, fluid and ephemeral; relevance of older role models has been reduced leaving people with a plethora of alternative models of behavior which may be media driven; daily challenges come in fresh and varied forms that rarely correspond to ready-made solutions; among others. Whereas discussions of lifelong learning have largely been driven by economic concerns, debates on the need to cope with an increasingly complex life occasioned by social and cultural changes are increasingly underscoring the importance of lifelong learning. According to Field (2000a), more people are taking part in a wider range of organized learning, which is not just occupational related. The forces of globalizing tendencies have also affected the cultural and social spheres of adult life, with implications for adult learning needs. A Growing Older Population According to Peterson and Masunaga (1998), the proportion of the older adult population called baby boomers has been increasing and becoming more visible. Levin (1998) correctly notes that the older, non-traditional students are also becoming prevalent at universities. Longer life expectancy and longer life after retirement is encouraging older adults to participate in learning activities. As Americans face financial strain in retirement income, many are staying in 6 the labor force longer and even changing jobs later in life (Giandrea, Cahill & Quinn, 2008). The change in jobs may be within the same occupation, across occupations, or can also include movement into self-employment. These economic driven changes have implications for the learning needs of older adults. They not only have to learn to facilitate second careers, but also adjust to new technological demands. For those who retire, pursuit for satisfactory ways of spending increased leisure time may lead to participation in learning activities. According to Roberson and Merriam (2005) increased free time from retirement, transitions in one’s family such as friendship with an adult child or dealing with grandchildren, and dealing with social loss (loss of spouse, withdrawal from social activities) and physical loss (loss of strength, health) are some factors that spur engagement in self-directed learning among older adults. Engagement in learning activities in later life is being touted as a way for helping the increasing population of older adults address their specific challenges in career and social life. Social and Economic Exclusion According to Kathryn (1999), some lifelong learning discourses emphasize social inclusion as a goal and, at least at policy level, there is concern for preventing people from being marginalized. A glance at a few statistics shows that the concern is not misplaced. A report titled a Nation at Risk by the National Commission of Excellence in Education (1983) identified 23 million adults in the United States as being functionally illiterate. Functional illiteracy ran as high as 40% among minority youth. In the high school system, 13% of 17 year olds were functionally illiterate. A lot of money was being spent on remedial education (basic skills in reading, writing, spelling, and computation), in college, military, business and industry. The above problems were compounded by a high drop-out rate from high school disproportionately affecting minority youth. A follow-up report (U.S. Department of Education, 2008) found that 7 high school drop-out rates are still a problem. For minorities in inner cities, about half do not graduate on time. The U.S. Department of Education (2008) cites some statistics to make the case that there are both private and public costs to high school drop-outs. For instance, in 2006, nearly 60% of high school drop-outs aged 25 years and over were either unemployed or not participating in learning at all. High school drop-outs were more likely to be unemployed, living in poverty, receiving public assistance, or in prison. The public costs here are loss of productive workers and higher costs associated with incarceration, health care and social services. In terms of private costs, high school graduates earn at least $8,100 more per year than high school drop-outs and about $ 1,000,000 less than college graduates over their lifetime. According to the OECD (2004), the earning gaps between those with and those without post-secondary education widen over a lifetime. The challenge here is that many adults will have to learn these skills necessary to function in society out of high school and in post-secondary institutions. To address this situation, the National Commission of Excellence in Education (1983) emphasized the importance of lifelong learning and called for an extension of learning opportunities beyond traditional institutions into homes, workplaces, libraries, museums, among other places. According to Desjardins, Rubenson, & Milana (2006), there is a risk of permanent exclusion or marginalization of segments of the population. Wider access to learning opportunities can reduce inequalities in living conditions as well as promote higher labor market rewards. A case exists for the need to encourage participation in learning activities especially for populations that have been left behind. The benefits for participation in lifelong learning at the societal and individual levels are immense. Lifelong learning has the potential of producing not only knowledgeable and skilled workers who will increase the economic competitiveness of a 8 nation, but also more fulfilled, aware and socially cohesive citizens (Tight, 1998a). The threat of economic and social exclusion hovers around those who do not take the responsibility to participate in learning throughout life (Desjardins, Rubenson, & Milana, 2006). Participation The importance of lifelong learning for achieving societal and personal goals is rarely in doubt. The key to drawing the benefits of lifelong learning is to have as wide a population as possible participating in learning activities continuously. In lifelong learning literature, there are calls for an understanding of non-participation and under-participation in learning opportunities. Tight (1998a) identified nonparticipation as a key problem in lifelong learning. Tight (1998b) cites the work of three national committees in the United Kingdom (The Widening Participation Committee chaired by Helena Kennedy; The National Committee of Inquiry into Higher Education chaired by Ron Dearing; and The National Advisory Group for Continuing Education and Lifelong Learning chaired by Bob Fryer) which in 1997 produced three reports (the Kennedy, Dearing and Fryer Reports) with policy recommendations on lifelong learning and without exception they all identified non- and under-participation as a key problem. Norman & Hyland (2003) called for a widening of participation to include those underrepresented in learning activities, not just increasing participation. Merriam, Caffarella, and Baumgartner (2007) reviewed six National Center for Education Statistics (NCES) surveys of adult participation in education and noted that there was an overall trend of increase in participation from a low of 10% in 1969, to 46% in 1999. Whereas the trend shows an overall increase in participation in adult education activities, the trend also shows that some groups are left behind. These groups are Hispanics, those with lower level of education, those with lower status jobs, and those employed part-time. Desjardins, Rubenson, and Milana (2006) after studying cross-national patterns of adult learning noted that adults with the 9 following characteristics are less likely to participate in adult education: older adults; women; low socio-economic backgrounds; less educated; less-skilled and in low-skill jobs; unemployed; minorities and immigrants. Inspired by international debates on lifelong learning, most governments and educational institutions introduced policy changes to encourage and embrace lifelong learning practices. The policy initiatives have largely focused on enabling a wider access to learning opportunities for all. Lifelong learning studies have been commissioned and papers published by the European Commission (Towards a Learning Society), Germany (a series of reports), Dutch, Norwegian, Finnish, British and Irish governments (Field, 2000a). A key issue among these papers was how to increase participation in lifelong learning programs. The Lifelong Learning Act of 1976 in the United States was created to help improve learning opportunities for individuals (Richardson, 1978). Increased flexibility in further education institutions has also helped overcome some of the institutional barriers thus increasing participation (Norman & Hyland, 2003). Problem Statement There is a new urgency to develop a better understanding of why some adults participate in lifelong learning and others do not (Rubenson, 2001, p.1). Studies focusing on lifelong learning policies have focused more on what the government and educational providers should do to enhance participation. Richardson (1978), in noting that problem, states that most of the lifelong learning approaches have focused on programs rather than learning and learners. In lifelong learning, emphasis is placed on the role of individual adults taking charge of their learning (Tight, 1998b). According to Field (2000a), “it is not the government that will produce more learning among the people, but citizens” (p. 23). It is an issue which requires citizens to act. Jakobi (2006) asserts that learning is an activity that has to be managed individually, at least according to the recent debates in lifelong learning. There are a multitude of studies focusing on 10 lifelong learning policies the government and educational providers should adopt. Such have not been matched by studies emphasizing the learner’s point of view. Some learner-centered lifelong learning studies which have been conducted have focused on developing an operational measure of physician lifelong learning (Hojat, Nasca, Erdmann, Frisby, Veloski, & Gonella, 2003); identifying characteristics of lifelong learners (Livneh & Livneh, 1988); identifying personality traits that might predict lifelong learning (Livneh, 1989); and developing a lifelong learning inventory (Crick, Broadfoot & Claxton, 2004). These studies, while offering great insights into lifelong learning from the learner’s point of view, have some limitations. Hojat et al (2003) for instance developed a lifelong learning instrument specific to physician’s-work-related learning only. Livneh (1989) used an operational definition of lifelong learning as number of hours per month engaged in learning activities for gaining professional skills. This vocational orientation elides learning activities engaged in for social or personal reasons which still constitute part of lifelong learning. The issue of readiness to engage in learning has also taken root in lifelong learning literature. “When we consider the central role that lifelong learning is assumed to play in the overall welfare of individuals, communities and society, then the readiness of adults to engage in it becomes a key issue” (Rubenson, 2001, p.1). The issue of readiness to engage in lifelong learning becomes important especially if you consider participation patterns discussed earlier. Studies that have taken the route of investigating the readiness of adults to engage in lifelong learning have used an operational measure of self-directed learning readiness. Such studies that have used a measure of readiness for self-directed learning to indicate readiness for lifelong learning include Litzinger, Wise, Lee, Simpson and Joshi (2001), Shokar, Shokar, Romero and Bulik (2002) and White (2001). This kind of operational measure is suggested in adult learning literature. The applicability of the self-directed learning concept to lifelong 11 learning has been discussed in writings and research (Merriam, Caffarella, & Baumgartner, 2007). Self-direction is taken to be both a goal and method in lifelong learning (White, 2001). Self-directed learning is the most common way in which adults undertake learning. However this conception of lifelong learning readiness leaves out at least two other concepts that may have applicability to lifelong learning readiness. A literature review supported the conceptual addition of adult’s readiness to respond to triggers for learning and readiness to overcome deterrents to participation concepts to the lifelong learning readiness concept. Adult engagement with learning is preceded by some triggers for learning (Aslanian & Brickell, 1980a; Jarvis, 1992; Kidd, 1959). Also, to engage in learning, adults often have to overcome some barriers to participation. A number of barriers can lead to non-participation even though the learner may want to participate (Desjardins, Rubenson & Milana, 2006). Hassan (1981) found a significant negative correlation between the number of obstacles perceived by adult learners and their readiness for self-direction in learning. This study conceptualizes readiness for lifelong learning as incorporating aspects of adults readiness to respond to triggers for learning, their self-directed learning readiness and their readiness to overcome deterrents to participation in learning. Conceptually, a lifelong learner should be able to identify triggers known to cause adults to learn as points in which they would engage in learning; be able to self-direct their own learning (since that is how most adult learning occurs); and be able to identify themselves as being able to overcome known deterrents to participation in learning. Purpose of the Study The main purpose of this study is to explore and determine the degree of readiness for lifelong learning of adult volunteers affiliated with a 4-H Youth Development Program in the southern region of the United States. 12 Research Objectives 1. To describe adult volunteers affiliated with a 4-H Youth Development Program in the southern region of the United States on the following demographic characteristics a) Age b) Gender c) Ethnicity d) Highest educational level completed e) Yearly net income f) Marital status g) Presence of children at home h) Employment status i) Length in current employment position j) Current occupational category k) Whether or not volunteer’s current employment requires continuous certification l) Number of times respondent has changed jobs in the last five years m) Length of time volunteering, and n) Format in which respondent prefer learning 2. To determine the readiness for lifelong learning of adult volunteers affiliated with a 4-H Youth Development Program in the southern region of the United States as measured by the Readiness for Lifelong Learning Scale 3. To determine whether differences exist in the readiness for lifelong learning as measured by the Readiness for Lifelong Learning Scale on selected demographic characteristics which include: a) Gender 13 b) Ethnicity c) Highest educational level completed d) Yearly net income e) Marital status f) Presence of children at home g) Employment status h) Current occupational category i) Whether or not volunteer’s current employment requires continuous certification j) Format in which respondent prefer learning 4. To determine if a model exists which would explain a significant portion of the variance of readiness for lifelong learning as measured by the readiness for lifelong learning overall item mean score and the demographic characteristics of age, gender, ethnicity, highest educational level completed, yearly net income, marital status, length in current employment, and format in which respondents prefer learning. Significance There is a paucity of studies investigating the readiness of adults to engage in lifelong learning. The results from this study will contribute to this limited body of knowledge. Prior studies of lifelong learning readiness have largely focused on learning for occupational reasons. This study goes beyond that by considering learning for the home and leisure spheres of adult life. It does this by incorporating the specific needs that lead to participation in learning in the conceptualization of readiness for lifelong learning. According to Livneh and Livneh (1988), “the responsibility and obligation of individual students, the professional school, the professional association, and educators are to develop professionals who are lifelong learners” (p. 638). Being able to assess readiness for lifelong 14 learning contributes to that obligation of developing lifelong learners by helping identify individuals with that orientation. There is the possibility that lifelong learning may be an integral component of employability in the future. This study will contribute to other efforts directed at assessing the readiness for lifelong learning of potential employees. Definitions of Terms The following definitions of terms are offered to assist in the understanding of the study. Lifelong learning: Refers to the acquisition of necessary knowledge, skills and attitudes to overcome challenges or take advantage of opportunities which present themselves in an individual’s life by moving in and out of learning opportunities (Cross and Hilton, 1983). Learning trigger: Refers to an event related to a past, present or anticipated change in life of an individual that requires new knowledge or skills to deal with. It is a change in an important sphere/area of an individual’s life that creates a need to learn. Learning opportunity: Refers to the various ways in which people engage in learning such as enrollment full-time or part-time in a college or vocational school, attending seminars and workshops within the community, training at places of employment, television courses, independent reading/study projects (library or online), correspondence courses, mentoring, goal-directed informal learning from colleague or friend among other ways. Self-directed learning: Refers to a process where the learner assumes responsibility for planning, implementing, and evaluating a learning experience (Brockett, 1984). Deterrent to participation: Refers to reasons contributing to adult’s decision not to engage in learning activities (Scanlan, 1986). Readiness for lifelong learning: Refers to a concept involving a readiness to respond to triggers for learning, self-directed learning readiness and a readiness to overcome deterrents to participation in learning 15 CHAPTER 2 REVIEW OF RELATED LITERATURE In this study, readiness for lifelong learning is conceptualized as incorporating readiness to respond to triggers for learning, self-directed learning readiness and readiness to overcome deterrents to participation in learning. This chapter aims at reviewing the literature on those three dimensions. Before a discussion of those three dimensions, the lifelong learning concept is introduced. Learning Defined From the times of Plato and even earlier, the study of learning has preoccupied myth-makers and scholars (Jarvis, 1992). It is a concept which defies precise definition (Knowles, Holton & Swanson, 2005). Illeris (as cited in Merriam, Caffarella & Baumgartner, 2007) defines learning as a process that brings together cognitive, emotional and environmental influences and experiences for acquiring, enhancing, or making changes in one’s knowledge, skills, values, and worldviews. Learning is the process of acquiring new knowledge and expertise (Swanson & Holton, 2001). Jarvis (1992) views learning as a process of transforming experience into knowledge, skills, attitudes, values and beliefs. It involves giving meaning to experience. Learning results in changes in behavior, knowledge, attitudes and beliefs whether occurring formally or informally (Merriam & Clark, 2006). Learning is distinct from education. It is wider than education (Jarvis, 1992). Education is an activity initiated by one or more agents with the desired effect being changes in knowledge, skills and attitudes of individuals, groups or communities (Knowles, Holton & Swanson, 2005). Education emphasizes the educator while learning emphasizes the individual in whom the change will take place. Learning is an experience of an individual while education is a social activity (Lalage, 2000). 16 Generally speaking, learning can occur through formal, informal or incidental means. Formal learning is typically institutionally-sponsored, classroom-based and highly structured (Marsick & Watkins, 1997). It is organized and conducted by an institution or agency (Spear & Mocker, 1984). The content to be learned and the manner in which it proceeds are externally determined. Informal learning is not highly structured and control of learning rests primarily in the hands of the learner. Informal learning also includes incidental learning, which is an accidental by-product of another activity (Marsick & Watkins, 1997). Lifelong Learning Defined Lifelong learning is a term used widely in educational discourse and has a range of meanings (Crick, Broadfoot, & Claxton, 2004). It is a term “fraught with difficulties” (Jarvis, 1992, p. 9). Lifelong learning is conceptualized as being a cradle to grave activity (Broschart, 1977; Cropley, 1980; Jackson, 2003; Merriam & Brocket 1997). The idea of education being confined to childhood, which Tight (1988b) calls “front-end” education, is hereby distinguished from involvement in learning throughout life. In an attempt to include all learning that occurs throughout life, such a definition introduces a hard to conceptualize, amorphous, boundary-less learning. Lifelong learning is differentiated from lifelong education. Cropley (1980) asserts that learning is internal to the individual while education refers to the experiences which shape learning. Lifelong learning is considered to be broader than lifelong education. It extends beyond formal education providers (Cropley, 1980). It includes learning which occurs in educational institutions, workplaces, homes, community and voluntary organizations (Jackson, 2003). Public schooling and adult and continuing education institutions are not exclusive players (Merriam & Brocket, 1997). All systems share the responsibility for helping people educate themselves (Houle, 1973). Some of the settings for lifelong learning include university, community colleges, 17 public schools, workplaces, community centers, public libraries, museums, and public broadcasting (Richardson, 1978). According to Richardson (1978), lifelong learning refers to the process by which “individuals continue to develop their knowledge, skills and interests throughout their lifetimes” (p. 15). It involves using both formal and informal learning opportunities to meet learner needs at each stage of the life cycle. Hiemstra (as cited in Livneh & Livneh, 1988) defines lifelong learning as a process that continues throughout one’s lifetime depending on individual needs, interests and learning skills. It is the intentional and self-directed pursuit of learning for continuing personal growth (White, 2001). It is all purposeful learning undertaken on an ongoing basis aimed at improving knowledge, skills and competence (Nijhof, 2005). Hojat et al. (2003) defined lifelong learning as “a concept involving a set of self-initiated activities (behavioral aspect) and information-seeking skills (capabilities) that are activated in individuals with a sustained motivation (predisposition) to learn and the ability to recognize their own learning skills (cognitive aspect)” (p. 434). Though consensus on a specific definition of lifelong learning is yet to be achieved, there is consensus that learning should continue throughout people’s lives. Most researchers provide specific definitions in individual studies (White, 2001). The lifelong learning definition for this study is adapted from Cross and Hilton (1983) and is stated as the acquisition of necessary knowledge, skills and attitudes to overcome challenges or take advantage of opportunities which present in an individual’s life by moving in and out of learning opportunities. 18 What Counts as Lifelong Learning Lifelong learning may have the appearance of a nebulous concept. It occurs in a myriad of manners and in a variety of settings. To the naked eye, it may appear as if it had no boundaries. Lifelong learning is differentiated from other forms of learning due to its deliberate and intentional nature. It involves a conscious decision to engage in a learning opportunity or activity. Even as it occurs in a variety of settings and ages, Cropley and Dave (1978) and Houle (1973) identified it as being purposeful. It’s not just a mass of random learning; individuals apply order to it (Lalage, 2000). According to Cropley (1980) such learning is “accompanied by a deliberate attempt to learn, awareness that learning is occurring and systematic attempts to facilitate it” (p. 2). All deliberate learning activities are included in lifelong learning (Richardson, 1978). It has to have what Tough (1979) calls a deliberate desire to gain and retain certain knowledge and skill. According to Schuetze (2007), lifelong learning also has to be lifelong and life-wide. Lifelong captures the essence that it is a learning that occurs throughout life. Life-wide regards the notion that it is a learning that occurs outside formal educational institutions and programs, and covers more than work-related outcomes. As will be discussed later, viewing lifelong learning as being instrumental in helping people address challenges or take advantage of opportunities as they arise in their lives addresses the lifelong and life-wide characteristic of lifelong learning. Kidd (1959), for example, sees learning as preparing people to meet their social roles. Fischer (1999) sees lifelong learning as occurring in the context of work or real world problems. The above descriptions give us some clarity as to the nature of lifelong learning. Whereas it is a process in which one may engage at all ages, in a multitude of settings, the intent on the 19 part of the learner has to be present. Lifelong learning is intentional, deliberate and purposeful whether it occurs in the workplace, on campus, at home, through formal or non-formal organizations, through traditional or non-traditional methods, or through the self-directed efforts of an individual (Richardson, 1978). It is also linked to life challenges or opportunities making it lifelong and life-wide. It is life-wide since it addresses learning beyond formal programs and it is directed at more outcomes than the traditional vocational or work–related outcomes. The Case for Lifelong Learning The necessity for lifelong learning is rarely challenged. The reasons for justifying lifelong learning may vary, but its importance is perennial in the widespread discussions and debates. A lifelong learning society is often deemed as an overall ideal that ought to be pursued. Richardson (1978) sees a lifelong learning society as being composed of three elements:  Individuals who foster their own growth and development;  Local providers who collaborate in offering learning resources; and  Federal, state and local governments which pursue policy strategies directed towards encouraging individual growth and enriching learning opportunities At the heart of calls for lifelong learning is a perceived need for individuals and nations to remain competitive in a global economy characterized by constant change and rapid technological development. According to Field (2000a), politicians view knowledge as the most important source of future advantage. Houle (1973) and United Nations Educational Scientific and Cultural Organization (UNESCO, 1975) pointed out the obsolescence of knowledge, rapid growth of new knowledge, multiplication and complexity of social problems as reasons why lifelong learning was a necessity. These factors would compel individuals to renew their learning throughout their lives (UNESCO, 1975). Peterson (1979) noted changes in traditional patterns of work and family life such as an increase in number of divorcee mothers joining the 20 workforce and increases in job changing in mid-career which required learning in order to adapt. Cropley (1977) includes goals of using lifelong education to promote equal access to social, economic and political advantages. McCombs (1991) identifies the need to continuously learn and retrain in order to develop qualified people for available jobs. Some of the push factors she identifies include deficiencies in basic reading, writing, and mathematical skills among the population, adaptability necessitated by changing demands of today’s workplace, and shortened product life cycle which may mean that future jobs would have to be restructured about every seven years. West (2006) calls an illusion the notion of a job for life. People have to be responsive and flexible to a changing labor market to survive. Fischer (1999) identifies problems in this age which can be addressed by lifelong learning as lack of creativity and change, coping with change and insufficiently supported school to work transition. More recently, Mulder and Bayer (2007) cite the changing content of work occasioned by economical and technological developments as the reason why lifelong learning is indispensable. Jackson (2003) summarizes the two strains of discussion which underlie the debates on the importance of lifelong learning. There is the individualist view in which lifelong learning is seen as a vehicle for individual fulfillment and provision of equality of opportunity. On the other hand, lifelong learning promotes the development of human capital which is believed to be the key to promoting success in a knowledge economy. The twin reasons supporting lifelong learning policy are the development of vocational skills for economic competitiveness and the fostering of inclusion and cohesion (Norman & Hyland, 2003). Lifelong learning has the potential of producing not only knowledgeable and skilled workers who will increase economic competitiveness of a nation, but also more fulfilled, aware and socially cohesive citizens (Tight, 1998a). The importance of lifelong learning for achieving societal and personal goals is not in doubt. 21 A Brief History of Lifelong Learning Lifelong learning as a concept is not new. The concept of learning throughout life is mentioned in ancient writings (Cropley, 1980). Early Islamic writings encouraged believers to seek knowledge at all ages. African societies institutionalized learning phases which continued until one became an elder (Lalage, 2000). There are examples of men and women who continued to learn until the end of their days in Greek, Roman, and Renaissance periods in the western world (Kidd, 1959). In Europe and North America, Lalage (2000) traces the idea of lifelong learning to movements for freedom in the 1900’s noting that an uneducated man, however well endowed in health and wealth, is still a slave to other people’s opinions. The emphasis was education for all as a necessary condition for human development. Field (2000a) traces its earliest discussions to the end of World War One, influenced by the debates held at the time about the extension of citizen rights to women and to working class men. Lifelong learning took center stage in international political discussions in the sixties and seventies (Mulder & Bayer, 2007). It was however a preserve of educational specialists meeting under intergovernmental bodies such as the United Nations Educational, Social, and Cultural Organization (UNESCO) and the Organization for Economic Co-operation and Development (OECD) (Field, 2000a). Inspired by international debates on lifelong learning, most governments and educational institutions introduced policy changes to encourage and embrace lifelong practices. Governments were attracted to the idea of maintaining economic competitiveness through lifelong learning. Despite the increased debates and widespread policy actions, according to Lalage (2000), lifelong learning is far from reality in any present day nation. 22 Lifelong Learning Policy Lifelong learning has continued to be placed at the center of many government’s agenda for education and training (Field, 2000b). Many nations have funded studies and came up with policy statements aimed at entrenching lifelong learning as a practice in their institutions as well as populations. Lifelong learning papers have been published by the European Commission (Towards a Learning Society), Germany (a series of reports), Dutch, Norwegian, Finnish, British and Irish governments (Field, 2000a). A key issue among these papers was how to increase participation in lifelong learning programs. According to Richardson (1978), the Lifelong Learning Act of 1976 in the United States was created to help improve learning opportunities for individuals. The Act emphasized federal support for learning opportunities such as adult basic education, continuing education, occupational and job training, parent education, special programs for individuals or groups with special needs among others. Federal financial support and most states focused more on postsecondary institutions than on less formal/traditional settings. What was lacking was support for an enlarged network of learning opportunities. Such a narrow focus on traditional postsecondary systems may provide a clue as to why policy in the United States has not resulted in increased participation. Whereas policy provided structure, it probably failed by not being responsive to most of the adult learners’ needs, thereby decreasing participation. In the United Kingdom, in 1997, the incoming Labor government appointed Dr. Kim Howells the country’s first minister of Lifelong Learning (Field, 2000a). Three national committees (The Widening Participation Committee chaired by Helena Kennedy; The National Committee of Inquiry into Higher Education chaired by Ron Dearing; and The National Advisory Group for Continuing Education and Lifelong Learning chaired by Bob Fryer) were also created which in 1997 produced reports (Kennedy, Dearing and Fryer Reports) focused on 23 influencing lifelong learning policy. They all endorsed the ideal of lifelong learning and the need for engagement in it by as many members of the society as possible (Tight, 1998b). The Kennedy Report of 1997 was charged with looking for ways of widening participation in further education especially those for whom achievement rates are less than the norm. The Dearing Report of 1996 looked into ways in which the purpose, shape, structure, size and funding of higher education could be developed to meet needs of the UK in the subsequent 20 years. The Fryer Report of 1997 was concerned with how to create a lifelong learning culture for all in the 21st century (Tight, 1998a). All three reports identified non- and under-participation as a key problem. They came up with a similar set of under-participating groups, as well as identified parallel strategies for action (Tight, 1998b). The three reports culminated in a consultative paper in 1998 by the Secretary of State for Education titled The Learning Age, which highlighted specific policy initiatives based on the earlier reports. Some of the policy commitments included an extra 500,000 places in higher education, launching a university for industry, individual learning accounts with 150 million pounds to support one million people among other initiatives. There was an emphasis on formal, accredited and vocationally relevant forms of provision (Tight, 1998b). The European Commission published its own white paper on education and training subtitled “Towards a Learning Society” (Field, 2000a) also focused on promoting lifelong learning in the European Union. China as a country has undergone enormous change in the recent past. According to Merriam, Caffarella, and Baumgartner (2007), the Sixteenth Congress of the Chinese Communist Party in 2002 declared that China would work towards promoting lifelong learning and creating a learning society. The government set up sixty-one experimental learning communities throughout the nation with the aim of encouraging people of all ages to make learning a priority in their lives. The centers engage people of all ages in nonformal and informal learning. 24 According to Field (2000a), Japan in policy offers a broader and more comprehensive approach to lifelong learning. In 1990, the Japanese government passed a law concerning the Development of Mechanisms and Measures for Promoting Lifelong Learning. An advisory board for lifelong learning was also created, which published recommendations to be adopted by universities, schools, local authorities, and other bodies. Japan’s approach was unique because its recommendations aimed at promoting opportunities for individual lifelong learning, and not just vocational education and training. In the policy, “flower arranging classes were promoted alongside access to new technologies; older adults were at least as much a focus as were employees or job seekers” (p. 30). But Field (2000a) continues to note that this legislation was no radical departure from existing practice. It was much in tandem with what is referred to as Social Education in Japan. There were also broader economic concerns with this move, two of which were the desire to create a cultural climate in which individuals took responsibility for their own development and the need to tackle an alleged lack of creativity in their workforce. The importance of lifelong learning to the economy and to individuals is not in doubt. The overriding theme of debates on lifelong learning has been to provide and enable access to a variety of learning opportunities, in a variety of settings in order to enable individuals to participate in the knowledge economies and also to lead fuller lives by meeting their personal developmental needs. For a long time the mainstream debates have embraced humanistic ideals of promoting equity. However, when it comes to policy, the broad view of lifelong learning is replaced with a narrower perspective on vocational education and training. According to Field (2000a), policy endorsement of lifelong learning is universal, but its implementation is patchy except when dealing with the skills of the workforce. Tight (1998b) adds that whereas rhetoric may have seemed inclusive, formal, accredited and vocationally-relevant forms of provision received more emphasis in policy. There has been a reduction from 25 the humanistic goals pursued in the 1960’s to mere economic imperatives (Nijhof, 2005). Kathryn (1999) adds that learning for economic competitiveness is what policy focuses on despite espoused commitments to diverse purposes of lifelong learning. This narrowing of focus of policy to formal programs and the sharpened focus on work-related skills may partly explain why the goals of lifelong learning originally envisioned as broad have not been achieved. Governments confine themselves to vocational training since it has considerable legitimacy and also it is one of the areas that governments feel impelled to act (Field, 2000b). The multitude of studies focusing on lifelong learning policies the government and educational providers should adopt have not been matched by studies emphasizing the learner’s point of view. Lifelong Learning Empirical Studies A number of studies have focused on trying to understand lifelong learning from the point of view of learners. Studies mostly focused on identifying and describing the personal and professional characteristics of lifelong learners, as well as attempting to build a profile of lifelong learners based on identified characteristics (e.g. Livneh & Livneh 1988; Livneh, 1989). Such studies were driven by the assumption that to be able to encourage or develop lifelong learners, it is imperative that we be able to describe such learners and know their characteristics (Livneh & Livneh, 1988). An instrument which can identify characteristics common to lifelong learners can be used to assess the presence of such characteristics in other subjects (White, 2001). Such instruments can aid in making hiring, training, or other work-related decisions, especially in careers that require workers be lifelong learners. Others have focused on investigating self-direction in lifelong learning, attempting to bring those two concepts together (White, 2001). 26 Livneh and Livneh (1988) conducted a study among human service professionals aimed at differentiating lifelong learners and low participants in learning. They also focused on developing a profile of lifelong learners in the human service professions. The Characteristics of Lifelong Learners in the Human Service Professions (CLLP) survey was administered to human service professionals who included social workers, teachers or professors, counselors, psychologists, private practitioners and nurses. Also measured were demographic variables (such as marital status, father’s educational background, participant job position) and number of hours per month spent in a variety of learning activities during the past one, three and five year periods. In distinguishing lifelong learners and low participants in learning, a factor analysis of the CLLP was conducted. It revealed seven factors including:  Professional growth through learning (I believe keeping updated and competent in my profession is important)  Self-motivated achievement (I am achievement motivated, determined to do well in my endeavors)  Educability (I have an interest in reading)  Readiness for change (I am able to cope with career and occupational changes)  Causation for learning participation (I became involved in opportunities for learning during times of personal crisis)  Familial educational background (My parents participated in learning)  Future orientation (I have a desire to advance my job) Of the seven factors, only five were identified as possible predictors of involvement in learning activities over the past five years (lifelong learning status). The low participants appeared to be deficient in these characteristics. These were Educability, Readiness for change, Causation for participation, Familial background and Future orientation. 27 Only educability and future orientation were significantly different between high and low participants in lifelong learning. According to Marra, Complese and Litzinger (1999), the factor analysis did not predict enough of the actual measured variance in lifelong learning to create a usable profile. Livneh and Livneh (1988) asked readers to interpret the findings with caution as the measure for participation in learning activities used in this study was gross (number of hours spent per month in learning activities during the past five years). The measure relies on the respondent’s ability to recall an extended list of events over a long period of time. The measure of learning includes both specific professional learning and personal growth activities. Livneh (1989) developed the Adjective Check List instrument that could be used to identify personality characteristics which can predict lifelong learners in the human service professions. The idea was to develop an instrument which could identify personality traits which might predict the amount of time spent in lifelong learning among human service professionals. In other words, the study sought to identify characteristics that can predict lifelong learning. A factor analysis revealed six interpretable factors including:  Extroverted/introverted which had adjectives such as opinionated, argumentative, aggressive, assertive, and persistent, among others  Social desirability which had adjectives such as dependable, conscientious, cooperative, and fair minded, among others  Organized with adjectives such as methodical, stable, rigid, patient, and logical among others  Self-centered had adjectives such as demanding, self-pitying, submissive, and rebellious  Reflective was associated with adjectives such as quiet, reserved, cautious, original, and insightful among others 28  Adventurous had adjectives such as spontaneous, impatient, excitable, energetic, and clever, among others Only the factor “organized” was significantly correlated with time spent in learning activities. Most of the personality variables explained very little variance in hours spent in learning activities. Livneh (1989) attributes the results to the gross measure of learning as hours spent per month in learning activities. The Adjective Check List may share overlapping adjectives which may reduce the viability of the factorial structure. Overall the study was not successful in identifying personality characteristics which can predict hours spent in learning or engagement in learning. According to Marra, Complese and Litzinger (1999), the studies by Livneh (1989) and Livneh and Livneh (1988) which aimed at producing lifelong learning predictive instruments for the human service professions produced inconclusive results. Crick, Broadfoot and Claxton (2004) developed an instrument, the Evaluating Lifelong Learning Inventory (ELLI) which could identify elements of an individual’s capacity for lifelong learning. The study identified seven dimensions capable of differentiating between efficacious, engaged and energized learners and passive, dependent and fragile learners. According to Crick, Broadfoot and Claxton (2004), a person’s learning orientation involves a complex mix of experience, motivation, intelligences, and dispositions. No instrument has been designed to assess the qualities which make up an individual’s capacity for lifelong learning. Their study aimed at identifying elements which define a good learner, and eventually designing an instrument which could assess an individual’s location at a given time on those elements. It aimed at identifying an individual’s lifelong learning orientation. They identified five dimensions of learning which include:  Learner commitment and engagement (growth orientation, meaning making and critical curiosity) 29  Fragility and dependence  Creativity  Learning relationships (independence, dependence or interdependence)  Strategic awareness The Crick, Broadfoot and Claxton (2004) study was critical in that the researchers were able to identify some main dimensions on which learners differ. The instrument could be used to differentiate learners based on their orientation to learning. However since this was a self-reported study, they identified a need to link these dimensions with more conventional measures of learning achievement. Also, the study involved respondents who varied in age (ranging from 6 years to participants in an Adult Vocational Program who were 18+). According to Merriam and Brockett (1997), adults differ from children in terms of how they learn. Marra, Complese and Litzinger (1999) view it as an imperative for graduates to demonstrate some recognition of the need for lifelong learning and have the ability to engage in it. They present the results of a survey they did on engineering graduates where the respondents affirmed an understanding of the need to engage in lifelong learning and also admitted to pursuing it in a number of ways. For example, 90% of respondents believed that the ability to teach oneself new skills at work was very important. Most believed they could teach themselves new skills needed on the job. Subscription to professional journals and participation in professional societies was done by at least 50% of the respondents. However, they called for an understanding of how students developed the necessary attitudes and skills. This may be revealed by an investigation of curricular and extra curricular activities in undergraduate education which promote the understanding and ability to lifelong learn. Hojat et al (2003) developed an instrument to measure physician lifelong learning and to identify its underlying components. The instrument was designed to measure lifelong learning 30 specifically for physicians. In their analysis they concluded that physicians’ lifelong learning is a multi-dimensional construct with five underlying factors/components. They include need recognition (cognitive), research endeavor (capabilities), self-directed learning activities (behavioral), technical or computer skills (skills), and motivation (predisposition). There was no significant difference between men and women on components of lifelong learning except for research endeavor. Physicians who published papers, presented findings at professional meetings, or collaborated in the conduct of research obtained higher mean scores in each of the five factors than those not involved in those activities. Hojat et al. (2003) also associated various factors with lifelong learning activities relevant to medical practice. For instance, self-initiation/self-directed component was associated with relevant activities such as receiving research grants, receiving professional awards or honors, journal editorial activities and serving as journal reviewers. Personal motivation component of lifelong learning was associated with holding office in national professional organizations, journal editorial activities, serving as a manuscript reviewer, and presenting research findings in public media or community groups. They called for an examination of these factors using more specific and relevant external criterion measures. They concentrated on lifelong learning for career purposes. Gopee (2003) conducted a study on nurses perceptions of lifelong learning at both the conceptual and practical levels. The focus here was on how nurses perceive the notion of lifelong learning and day-to-day factors which would facilitate its implementation among registered nurses. The qualitative study revealed three sets of factors which could foster lifelong learning in nursing. They include organizational, socio-political and individual or personal factors. If the three are taken into account at an operational level, lifelong learning is likely to become a reality. 31 Lifelong learning is a requirement for trainers. They have to keep developing their competencies to cope with ongoing developments in society and meet the changing demands of their clients. Mulder and Bayer (2007) investigated the relationship between trainers’ attitudes towards lifelong learning and their competencies. They also investigated trainers’ attitudes towards lifelong learning and the need that trainers feel for training. They found that the trainers generally had a high attitude towards lifelong learning and that they rated their competencies highly. They found a positive relation between trainers’ attitudes and their competencies. Hence it is likely that trainers who had a positive attitude towards lifelong learning took care of their competencies. There was no clear relation between the attitudes and the needs for training. Lifelong learning studies have not just concentrated on vocational concerns. In a study linking lifelong learning and some health outcomes, Hammond (2004) found that participation in lifelong learning had effects on wellbeing, protection and recovery from mental illness and capacity to cope with the onset and progression of chronic illness and disability. Participation also had an effect on psychological qualities such as social integration, self-efficacy, self-esteem, competencies, and a sense of purpose and hope. These are the immediate psychosocial outcomes which mediate and promote circumstances conducive to positive health outcomes. The focus of most studies has been identifying personal characteristics or qualities that could be used to predict participation in lifelong learning. These characteristics could be used to distinguish lifelong learners from non-participants based on the presence or absence of such characteristics. This is an earnest approach to investigating participation by looking for qualities associated with lifelong learning participation. Some studies have used self-directed learning as an organizing concept for lifelong learning. An investigation by White (2001) using the Oddi Continuing Learning Inventory (OCLI) found no significant difference on lifelong characteristics between small group bible 32 study participants and non-participants. The relationship between lifelong learning and participation in small groups was not supported. He also found that participation was motivated by establishing community and encountering new ways of thinking rather than content acquisition. An assessment of self-directed learning readiness was used to indicate readiness to engage in lifelong learning (e.g. in Litzinger, Wise, Simpson, & Joshi, 2001; Shokar, Shokar, Romero & Bulik, 2002; and White, 2001). An assessment of lifelong learning readiness which includes assessing readiness to respond to learning triggers, self-directed learning readiness and readiness to overcome barriers to participation in learning is undertaken in this study. Readiness to Respond to Triggers for Learning The main impetus for the lifelong learning movement is that it enables individuals and countries to deal with the challenges associated with constant change and global competition. Lifelong learning is seen by many as being essential for the survival and effective functioning of individuals in their workplace, at home and in their personal lives especially in the current environment characterized by constant change. Countries have adopted policies aimed at developing a lifelong learning society, which is viewed as a necessity if countries are to cope in an environment characterized by rapid technological development and global competition. Lifelong learning is thus discussed as being instrumental and necessary, a learning necessitated by the need to cope with constant changes occurring in an individual’s/country’s environment. Adult engagement in learning is instrumental in nature and is characterized by a necessity to learn. It is usually in response to some trigger to learn. Aslanian and Brickell (1980a) reviewed the literature on adult life cycle in pursuit of a better explanation of the timings of adults’ engagement in learning. Their review included the work of such scholars of life cycle as Daniel Levinson, Roger Gould, George Vaillant, and Gail 33 Sheehy among others. It emerged that adult life is divided into stages through which adults pass at relatively fixed times. The stages are rooted in the biological, social and psychological nature of adult human beings. Passing from one stage to another involves a significant transition which poses challenges and offers opportunities for growth. Transition to a different status or stage requires learning of new knowledge, skills, attitudes and values. The learning can be self-directed or other directed. There are also psychological and biological (internal) events and social and economic (external) events which may trigger learning. It is the triggers, changes in life circumstances, which cause the decision to learn at a particular point in time. An individual may be willing to transition to another stage or status, but not take up learning to enable that transition, till a trigger precipitates it. Most adults Aslanian and Brickell (1980a) surveyed, regardless of their demographic characteristics, identified their own changing circumstances as their reasons for learning. People learn when some events trigger a need to learn or a need to learn arises. Adult learning scholars such as Kidd (1959), Aslanian and Brickell (1980a), and Jarvis (1992) among others, have written that adult engagement with learning is preceded by some triggers for learning. Some of the triggers emanate from changing adult life circumstances. According to Aslanian and Brickell (1980a) opportunity to learn and even desires are not sufficient to cause most adults to learn. A triggering event has to occur that converts latent adult learners into active learners. This view is supported by Jarvis (1992) who asserts that experiencing a need or want to learn does not necessarily result in participation in learning activities. He contends that non-learning is a fairly common phenomenon even when a need to learn has been experienced. Participation in learning given the presence of triggers is not automatic. It involves a decision to engage in a learning activity. 34 A learning trigger is defined as an event related to a past, or present anticipated change in life of an individual that requires new knowledge or skills. It is a change in an important sphere/area of an individual’s life that creates a need to learn. A trigger is an event that causes learning to occur at a particular time rather than later (Aslanian & Brickell, 1980a). Triggers are considered to be circumstances which act as catalysts to learning (Knox & Videbeck, 1963). Learning is thus instrumental in that it enables adults to make necessary adjustments and cope with changes in life circumstances. Knox and Videbeck (1963) discussed critical changes in life circumstance such as starting a new job, marriage, birth of a child, a move to a new community, retirement as being useful in explaining participation in learning. Tough (1979) asserts that adults set out to learn when confronted with decisions of intense personal importance. Examples of such include choosing a career, deciding which university to enter, considering whether or whom to marry, selecting a place to live or planning for retirement. They move people towards learning. A nationwide study by Aslanian and Brickell (1980a) seeking to understand the causes and timings of adult learning showed that 83% of learners surveyed described some past, present or future change in their lives as reasons to learn. The majority of learning was to help cope with life changes/transitions. Areas of transition mentioned included career (56%), family (16%), leisure (13%), art (5%), health (5%), religion (4%), and citizenship (less than 1%). More adults undertook learning to make career transitions. The survey results showed that the decision to learn at a certain point in time was triggered by a specific event, related to a transition in the survey participant’s life. Transition here is used to refer to movement into a new status that an adult wishes or must enter. Transitions require learning. A trigger is an event that causes learning to occur at the time it does rather than later. Most of the learning that occurred in the above transitions was caused by a trigger. Examples of triggers that necessitated learning in the career 35 sphere included getting a new job, getting additional responsibilities, purchase of new machinery or getting promoted. In a study of Human Service Professionals, Livneh and Livneh (1988) identified that the “causation for learning participation” was a distinguishing factor between lifelong learners and low participants in learning activities. The most heavily loaded item on that factor was “I became involved in opportunities for learning during times of personal crisis.” Ellinger’s (2005) discussion of informal learning in the workplace identifies both internal and external catalysts for learning. Some of the catalysts she names include challenging assignments and new positions or responsibilities. Adults responding to triggers for learning may be a distinguishing factor between lifelong learners and low participants in learning. Lifelong learners are expected to preponderantly respond to triggers for learning with engagement with learning. Knowles (1990) detailed six assumptions about adult learners that comprise his andragogical model. At least two of these have pertinence here since they conceptually tie adults’ readiness to learn with life circumstances. The first, which he labeled readiness to learn, involves the idea that adults become ready to learn things they need to know in order to cope effectively with their life situations. He also mentions that developmental tasks are a rich source of readiness to learn. The second assumption is called orientation to learning. He explains that adults will devote energy to learn if they perceive that it will help them perform tasks or deal with problems in their life situations. In other words in their learning adults are task-centered, life-centered or problem-centered. This view supports Tough’s (1979) assertion that studies have shown that “some anticipated use or application of the knowledge and skill is the strongest motivation for the majority of learning projects “ (p. 39). He adds that adults are motivated to learn not to master an entire body of subject matter, but rather to solve fairly immediate problems, tasks or decisions that demand certain knowledge and skills. 36 Tough (1978) reviewed studies that had been done on adult’s learning efforts and concluded that self-planned learning efforts constitute about 80% of all adults’ major learning efforts. The main motivation for such learning projects was anticipated use or application of the knowledge or skill being learned. Learning a skill is thus undertaken to gain knowledge or skill to perform a task. Examples he gives of such tasks include raising a child, writing a report for a boss, handling a case, fixing something around the house or sewing a dress. Learning knowledge for its own sake is a less common motivation. Learning for certification was rare ranging from less than 1%-15% of all learning projects. Most adults learn not just for the sheer love of knowledge or possession of knowledge, but because they want to use the knowledge (Aslanian & Brickell, 1980b). Implied in these assumptions is that life circumstances can be sources of not only a need to learn but also motivation to learn. In essence, life circumstances which are part of an individual’s environment birth learning needs. Findsen and McCullough (2007) found that participation in formal learning for older adults was linked with trigger events or episodes in the learners’ lives. Participation was attributed either directly or indirectly to a significantly altered daily routine and circumstance. Approximately half of the respondents identified a recent trigger episode or event that influenced their decision to engage in learning. “The high prevalence of trigger events reported by learners as prompting their decision to engage in formal learning would seem to suggest a strong relationship between such events and learning propensity” (p. 202). The events made people proactively seek a program or become open to suggestions by family and friends. The Findsen and McCullough’s (2007) study was aimed at investigating the motivations and trigger events for engaging in learning for older adults (aged 50 years or more) in higher education and further education (formal education) since they were minimally represented in higher education institutions especially in West of Scotland. The respondents mostly specified 37 motivations for participation which had a goal (instrumental) orientation. For the majority of the students participation was either wholly or partly related to life transitions. Forty percent of respondents cited work-related reasons for participation including such reasons as desire to facilitate a career change and achievement of financial security through regular work. Family considerations were identified as a further goal-related motivation that heralded engagement with learning. Learning was undertaken with the desire that it would facilitate sustained or developing relationships. For example some learned computing so they would keep in touch with children in distant lands. Findsen and McCullough (2007) found that motivation “seemed to be responsive to life events and resultant changes to lifestyle and environs” (p.204). Triggers mentioned by students were acutely personal or related to immediate family. According to Roberson and Merriam (2005), researchers may want to investigate how life stages shape learning, especially self-directed learning. In a study of self-directed learning activities of older adults, they found that their learning pursuits were not just random acts to occupy extra time, but were personal educational pursuits motivated by unique issues in their lives. They engaged with learning to help adjust to changes in their lives. Merriam (1978) contends that knowledge of adult stages of life can become a valuable resource for diagnosing learning needs. She acknowledges the contribution of the works of Erikson and Havighurst’s (among others) on life-span development in helping understand adults in the middle age and their engagement in learning. Havighurst (1980) saw a “need for different educational goals and practices at different stages in the adult life span” (p. 5). Havighurst presents each decade of life as having attendant dominant developmental tasks the successful achievement of which leads to happiness and success with later tasks, and failure leads to unhappiness and difficulty with later tasks. Erikson developed seven stages based on critical periods of ego development. There are a series of psychosocial tasks that have to be successfully 38 resolved in these stages failure to which they created problems for later life periods. Havighurst (1980) sees the role of adult education as helping people master developmental tasks of each stage in an adult’s lifespan. Most adulthood tasks come from a combination of social expectations and personal values (Merriam & Mulins, 1981). One may view the transitions that occur in adult life stages as changes in life circumstances which give rise to learning needs. Studying such transitions may help us understand adults’ engagement with learning. Adults participate in learning based on the assumption that education has a positive value in solving problems (Kidd, 1959). In Kidd’s conceptualization, solving problems or performing in social roles creates a need (trigger) for learning. He sees useful consequences in relating the concept of self-learner to that of social roles. Social roles (such as parent) have tasks that one has to learn to perform. He calls a “teachable moment” that point when one has to learn within a limited time to fill a role. Kidd (1959) asserts that all adulthood is not identical. He decried the lack of studies addressing the full-life development of man in a comprehensive way, at least as it affects learning. In understanding learning needs we need to consider the whole span of life, identifying role-specific learning needs in the various stages of an adult’s life. What we can draw from this research is the importance of considering life-stages and life-roles as spurring the need for learning among adults. According to Jarvis (1992), learning begins when one encounters experiences for which one has no preset responses. This is when knowledge and skills gained from previous experiences are insufficient to deal with new and unfamiliar experiences. This is a lack of accord between the external world and the internal biographies gathered over time. He calls this a point of disjuncture, which presents an ideal condition for learning. Disjuncture can be self-induced or induced by other factors. Disjuncture can also occur between anticipated experience and one’s biography. Only after they have made a response to the disjuncture either by learning or deciding 39 that they cannot or do not wish to learn, can life continue normally. The reaction to disjuncture may involve enrollment in some educational course or obtaining resources for other types of learning. Non-learning is a fairly common phenomenon even when disjuncture has been experienced (Jarvis, 1992). He contends that since change occurs in a rapid rate in the modern world, disjuncture is inevitable. Given that disjuncture is inevitable, how one responds to disjuncture (either by learning or not) will be a distinguishing characteristic of lifelong learners. It is conceivable that changes in life circumstances such as getting your first baby or getting promoted may get one to a point of disjuncture which may call for new learning. In investigating self-directed learning activities of adults who had less than a high school completion, Spear and Mocker (1984) obtained findings that shed more light on the triggering events that set the self-directed learning process in motion. They found that the triggering event for a learning project comes from some change in the life circumstances of an individual. This may be change happening to an individual or someone who affects that individual’s life, and it may be positive or negative. It could also be an event that is observed in the physical, social, or psychological environment (life space) in which an individual functions. The psychological, physical and social elements in that field determine action or human behavior. Spear and Mocker (1984) borrow from Kurt Lewin’s view that to understand human behavior, we should focus on an individual’s life space or field. A simple description of observable behaviors (such as starting a learning project or attending a course) obscures the significance of the circumstances in which it occurred and the meaning of such actions. Spear and Mocker (1984) reported that participants in their study identified changes in life circumstances as the triggers that preceded engagement with a learning project. Richardson (1978) asserts that people feel a need for learning opportunities during middle and later stages of life to advance their careers, cope with change, learn new skills, and lead 40 fuller lives. Kidd (1959) gives examples of tasks in the middle age which may involve substantial changes and require considerable learning such as reaching ones career peak, making satisfactory use of leisure time, adjusting to physiological changes, and helping one’s teenagers become responsible adults. Such a list of learning opportunities, he says, can be generated for each overlapping stage of life. Roberson and Merriam (2005) contend that older adulthood, as other life stages, brings with it changes and transitions in work, family and health. In their study, participants raised three main changes in later life with which self-directed learning helped them cope. They include retirement which created a lot of free time they used to pursue self-directed learning opportunities; transitions in one’s family such as friendship with an adult child or dealing with grandchildren; and experiences of social and physical loss such as loss of strength heralded learning about good health, or being widowed, or withdrawing from social activities as a result of retirement. These transitions become the impetus for self-directed learning, a learning undertaken to deal with developmental challenges. Self-directed learning allows older adults to address their individual learning needs in their life stage. In every interview that Roberson and Merriam (2005) conducted, the incentive to learn, the interest in learning and the catalyst was related to late-life change. Knowles as cited in Roberson and Merriam (2005) states that learning is usually in response to ones situation in life and the particular stage in one’s life becomes the context for learning. Merriam and Mullins (1981) conducted a study in which they found that of all adults sampled, nearly all age, income, and gender subgroups considered Havighurst’s developmental tasks as important in their consumption of learning programs. Rager (2003) found that women diagnosed with cancer depended on self-directed learning to cope. The motivations for them to participate in self-directed learning were to lessen their fear, help make and validate treatment decisions, and understand what was going to happen in their treatment. Those three objectives were achieved, and in addition two more outcomes 41 were achieved; a growth in self-confidence and the desire to help other breast cancer patients. Hence, self-directed learning helped the women in those difficult circumstances. Missing from the adult education literature is research that describes the impact of a crisis situation on the self-education process (Rager, 2003). Andruske (2003) studied 23 single women, aged 23-55, who were navigating structures to leave welfare between the years 1998 and 2001. In her study, she found that these women used self-directed learning activities to gain knowledge that enabled them to strategize on ways to gain control over their lives. They used self-directed learning activities to learn about their welfare entitlements, health, their legal rights and employment and work skills. For instance they were able to become political agents by learning about their welfare-entitlements policy in a self-directed manner since welfare workers sometimes failed to inform them about available resources or options. One lady, in an attempt to cut down costs while going through a divorce, spent hours at the law library learning about her rights. She attributes her winning the support she was seeking in her divorce to the skills she learned in her self-directed learning activities. Another lady became an expert on her anxiety and eating disorder through self-directed learning projects and used that knowledge to raise awareness and advocate for her benefits to the regional health officials. These self-directed learning endeavors were instrumental in addressing the challenges that these women faced. Some studies mentioned above such as Aslanian and Brickell (1980a), Rager (2003) and Roberson and Merriam (2005) identify that adults engage with learning to respond to some triggers related to changes in life circumstances. The timings of engagement with learning coincide with the presence of such triggers. Adults’ circumstances continually change throughout the lifespan and these changes create learning needs. When adults engage in learning to address learning needs that result from changes in life circumstances; with such changes being a 42 perennial feature of adult life, they end up participating in learning throughout their lives (lifelong). Learning triggers are thus conceived as antecedents to lifelong learning since changes in life circumstances (triggers) feature consistently throughout one’s lifespan. Therefore anyone who undertakes learning in response to learning triggers is likely to be classified as a lifelong learner since adults experience changes in life circumstances throughout life. Considering lifelong learning as being tied to changes in life circumstances (current or anticipated) offers a broader conceptualization that captures a wider range of adult learning (life-wide learning), broader than conceptualizations that have focused on vocational or work-related learning only. Rubenson (2001) criticizes the Canadian Adult Education and Training Survey (AETS) for offering prominence to career-related motives for learning, sidelining other possible motives. It also focuses on organized education and training, which in reality constitutes a small fraction of total adult participation in learning activities. This criticism can be applied to many surveys of adult learning participation. The popularity of practical, how-to-do-it books (such as cook-books and child-care books) as evidenced by purchases points to how commonly adults learn for home and personal responsibilities (Tough, 1979). Lifelong learners engage in learning to address challenges or opportunities that come with changes in their life circumstances in such broad areas as jobs and careers, home and personal responsibilities and use of leisure time. According to Aslanian and Brickell (1980a) the three highest areas of triggers for learning were career, family and leisure. Changes in life circumstances (triggers for learning) are incorporated in the lifelong learning readiness conceptualization for two reasons. First, changes in life circumstances are a lifelong source of learning triggers. They have been identified as triggering a need to learn and they feature throughout life. Secondly, the current vocational-based lifelong learning conceptualization fails to capture all the other non-work-related learning that adults undertake. 43 Considering changes in life circumstances as triggering learning enables a more inclusive conceptualization of lifelong learning including eschewed areas of adult learning such as home and personal responsibilities and leisure. Both reasons attend to a life-wide and lifelong conceptualization of lifelong learning. Assessing readiness for lifelong learning should involve a consideration of an individual’s readiness to respond to triggers for learning which result from changes in life circumstances. Lifelong learners should be able to identify learning as a way they would deal with changes in life circumstances (learning triggers). Tough (1979) and Aslanian and Brickell (1980a) in their surveys of adult learners identified three broad areas of adult life, the transitions in which were rated highly as triggering learning. Job and Career Majority of respondents in both studies identified career related transitions as their reasons for deciding to learn. This is anticipated given the importance accorded to jobs and careers for success of individuals and the economy. Some triggers for learning drawn from studies by Aslanian and Brickell (1980a), Findsen and McCullough (2007), Knox and Videbeck (1963) and Tough (1979) that fall under this category include:  Entering a new job/occupation e.g. getting hired in a new position or changing a career  Adapting to a changing job e.g. where there are changes to an existing job such as getting new equipment, opening a new plant, passage of new regulations, maintaining or upgrading competence to keep up with a profession  Advancing in career e.g. getting promoted, getting a new major responsibility, addition of new personnel to be supervised  Dealing with immediate specific tasks or decisions for which one may be unprepared hence requiring new learning 44 Home and Personal Responsibilities People also learn knowledge and skills to help manage their home and family. Tough (1979) points to the popularity of home reference books on such topics as medicine, home repairs, housekeeping, marriage, sex, and gardening as indicating that learning in such areas is undertaken in a more widespread manner than is commonly acknowledged. Some examples of triggers for learning identified from studies by Aslanian and Brickell (1980a), Findsen and McCullough (2007), Knox and Videbeck (1963) and Tough (1979) that fall under this category include:  Spouse-related e.g. getting married, becoming pregnant, maintaining a joyful relationship and handling conflicts with a spouse, improving sex life, or dealing with divorce may trigger involvement in learning  Children-related e.g. childbirth, assisting children go through school, helping teenagers become responsible and effective adults, developing a joyous relationship and handling conflicts with children  Finances-related e.g. learning triggered by reduction or increase in family income in areas such as budgeting, stock markets and investing, and insurance. Also this includes decisions involving heavy expenditures such as buying a house, car or important equipment  Health-related e.g. dealing with current loss or past loss of personal health (regain or maintain health), dealing with anticipated health loss, injury or illness of a family member, and adjusting to physiological changes of middle age. Leisure or Interest Some transitions may require learning skills in sports, crafts, hobbies and social activities (Aslanian and Brickell, 1980a). Triggering events may result from transitions outside leisure 45 such as loosing a spouse, moving to a new neighborhood, retirement or even divorce. Also changing one’s leisure activities may be a trigger that requires learning to succeed in new leisure activities. Some examples of learning identified from studies by Aslanian and Brickell (1980a), and Tough (1979) that fall under this category include:  Sports which includes swimming, bowling, tennis, skiing, sailing, scuba diving, surfing etc.  Music which may consist of dancing lessons, singing lessons, learning how to play a musical instrument  Traveling which can be learning before taking a trip, hiking, etc  Decorative art and craft, flower arranging, painting, or photography, e.t.c. Lifelong learning is instrumental in helping adults deal with challenges and opportunities present in their lives. The major areas of learning that adults identified themselves as engaging in from studies such as Aslanian and Brickell’s (1980a) can be categorized under career, home and family responsibilities, and leisure/interest. However, engagement in learning in these three areas of adult life can be attributed to an underlying set of triggers for learning (changes in life circumstances). Examples of triggers drawn from Aslanian and Brickell’s (1980a) work and others, to elaborate this point include:  A drop in family income could trigger: learning how to prepare different food to cut on costs (cookery falls under leisure learning); taking up sewing lessons in order to stretch the clothing budget (leisure learning); or learning new skills to enable one to pick up a better paying job or a second job (career-related)  An increase in family income could trigger: enrollment in learning activities for investing and securities at a local bank to help make investment decisions (Home and family 46 responsibilities- finance-related); engagement in learning activities such as fishing, golf, antiquing (leisure learning)  Retirement of self or spouse could trigger: learning activities related to budgeting to help live within a reduced income (Home and family responsibilities- finance-related); learning a craft or sport to occupy increased free time (leisure learning)  Moving into a new house or apartment could trigger: learning how to fix up an old house using do-it-yourself books or manuals to save on costs (home and family responsibilities); or learning about gardening if it has a garden (leisure learning) A list of triggers that can cause engagement in learning activities among adults include: major changes within one’s job; a promotion; moving into a new job; request from employer to participate; seeing peers get ahead in their careers; getting a divorce; getting married; becoming pregnant; moving to a new location; increase in family income; rising cost of living; retirement; loss of health through injury or illness; suggestions from friends and relatives, among others. This list is not conclusive; there are many more triggers that prompt adult engagement in learning activities. The above mentioned triggers will form the basis for questionnaire items for assessing readiness to respond to triggers for learning. In assessing readiness to respond to triggers for learning the focus is on the extent to which individuals identify themselves as likely to engage in learning if confronted with such triggers for learning. It is expected that lifelong learners, when presented with such empirically derived triggers for learning, will rate themselves as highly likely to participate in learning for most of the triggers to address the challenges that arise from them. Lifelong learners are likely to view learning as a viable solution in addressing the challenges or opportunities they face in the three spheres of adult life identified above. Given the above triggers, identifying the likelihood of engaging in learning will indicate an inclination 47 towards life-wide learning as the learning here is focused on more than work-related outcomes. Given that these triggers are a perennial feature of adult life, responding to them with learning indicates that one may continue learning throughout life (lifelong learning orientation). Thus they thus see learning as a viable way of meeting their challenges and taking advantage of their opportunities. “If people do not perceive participation in adult education as a means to satisfying their needs, and/or if they do not believe themselves capable of engaging in education or training, they will rarely participate unless forced to do so” (Rubenson, 2001, p. 21). The next section addresses another proposed component of lifelong learning readiness; the readiness of adults to self-direct their own learning. Self-Directed Learning and Lifelong Learning Writings and research on Self-Directed Learning demonstrate an interest in the concept’s applicability to lifelong learning (Merriam, Caffarella, & Baumgartner, 2007). There are many overlaps between discussions of self-directed learning and lifelong learning. Self-direction is both a goal and method in lifelong learning (White, 2001). Lifelong learning rests on the belief that individuals are and can become self-directing (Cropley, 1980; Tight, 1998a). In fact, Kidd (1959) says it is part of the nature of man to grow towards self-direction, self-discipline and autonomy. In lifelong learning there is an emphasis on individuals to take charge of their learning (Tight, 1998b), an idea that Brockett and Hiemstra (1991) offer as defining self-directed learning. Considering the lifespan, most of the learning that takes place may be described as taking forms of self-directed learning (White, 2001). “Learning on one’s own is the way most adults go about acquiring new ideas, skills and attitudes” (Merriam & Caffarella, 1991, p. 41). Smedley (2007) asserts that to enable lifelong learning, students have to be taught how to learn 48 independently. According to Candy (1991), self-directed learning is seen as a means and an end of lifelong education. Self-directed learning, just like lifelong learning is grounded in humanistic philosophy (Merriam, Caffarella, & Baumgartner, 2007; Piskurich, 1993). Some of the tenets of humanistic philosophy include a belief that human nature is good, humans have unlimited potential, and that individuals have free will and can take responsibility for their learning (Merriam, Caffarella, & Baumgartner, 2007). Humanism holds sacred the autonomy and dignity of human beings (Elias & Merriam, 1980). Autonomy of learners is central to both self-directed learning and lifelong learning. According to Oddi (1986) self-directed learning is commonly associated with attributes of being autonomous, self-actualizing, seeking opportunities to grow and fulfill potential. The same are found in descriptions of humanistic philosophy. Both self-directed learning and lifelong learning are grounded in humanistic philosophy. The prevalence of self-directed learning among adults has been well established (Brookfield, 1984; Rager, 2003). Hassan (1981) found a significant predictable relationship between readiness for self-direction in learning and the number of learning projects conducted in a year. It is one of the most common ways in which adults pursue learning throughout their lifespan. It is not far-fetched to conceive self-directed learning as being instrumental for lifelong learning. To be a lifelong learner, one has to take charge of his/her own learning. Given that majority of adult learning is self-directed, a lifelong learner has to be capable of being self-directed in his/her learning. A measure of self-directed learning readiness needs to be included in the assessment of lifelong learning readiness. Self-Directed Learning Defined Self-directed learning is rarely defined precisely (Grow, 1991). Oddi (1987) contends that definitions are frequently confusing and overlapping. Names used interchangeably with self- 49 directed learning include independent learning, self-planned learning, self-instruction, autonomous learning, self-teaching, self-study, self-education, discovery learning, and the inquiry method (Guglielmino, 1978). Grow (1991) views self-directed learning as the degree of choice a learner has within an instructional situation. Piskurich (1993) views self-directed learning as a training design where trainees master predetermined material at their own pace without the aid of an instructor. Self-directed learning is a “form of study in which learners have the primary responsibility for planning, carrying out, and evaluating their own learning experiences” (Merriam & Caffarella, 1991, p. 41). A successful self-directed learner is one who has an awareness of what he wants to learn and knows how to go about it (Brockett, 1985b). Brockett and Hiemstra (1991) describe self-directed learning as a way of life in which adults make a conscious choice to take responsibility for their own learning. Common to all self-directed learning definitions is some type of student involvement or choice (Piskurich, 1993). Self-directed learners take control and accepts the freedom to learn what they view as important for themselves (Fisher, King & Tague, 2001, p. 516). The learner takes responsibility for his/her learning. Self-directed learning takes place in and outside of the confines of formal learning institutions (Ellinger, 2004). It also does not mean learning in isolation as learners may draw from helpers and other resources (Merriam & Caffarella, 1991; Ellinger, 2004). There are two approaches to studying SDL. One approach investigates SDL as a personality characteristic (e.g. Oddi, 1986; 1987) while the other focuses on SDL as a process of study (e.g. Tough, 1977; Piskurich, 1993). Prevalence of Self-Directed Learning The study of self-directed learning is relatively new, though its practice is not a new phenomenon (Guglielmino, 1978). This may be because much of self-directed learning is 50 invisible. It is largely embedded in people’s daily life and occurs outside formal institutions (Merriam, Caffarella, & Baumgartner, 2007). Allen Tough, terming it self-planned learning, was one of the first to conduct a comprehensive description of self-directed learning (Merriam, Caffarella, & Baumgartner, 2007). Tough (1978) reviewed a cross-section of studies which investigated the number of major learning efforts adults partake in a year, what they learn, how much time they spend and how they plan for it. He concluded that though numbers may change from one population to another, between 70-100% of participants undertake one major learning effort per year. The typical learner conducts five distinct learning projects in a year. Of that learning only 20% is planned by a professional, 73% is planned by the learner. According to Brookfield (1984) Tough’s work was instrumental in that it challenged the assumption that adult learning could only occur in the presence of fully accredited professional teachers. The view that institutionally sponsored programs were more deliberate and purposeful as opposed to learning in non-institutional contexts was also challenged by his work. Livingstone’s study (as cited in Rager, 2003) found that 95% of the sample used in the study engaged in some type of informal learning. Participation in self-directed learning is almost universal, as 90% of the population is estimated to be involved with at least one self-directed learning activity per year (Merriam & Caffarella, 1991). The prevalence of self-directed learning in adults lives was confirmed by studies subsequent to Tough’s. Merriam, Caffarella, & Baumgartner (2007) and Brockett (1985a) cite several such studies. Self-Directed Learning Models Researchers also concerned themselves with developing in-depth self-directed learning conceptual models (e.g. Brocket & Hiemstra, 1991; Candy, 1991; and Garrisson, 1997). In initial studies, it was assumed that the process of self-directed learning was similar to the formal 51 learning process, a process linear in nature involving establishing goals, locating resources and choosing learning strategies (Merriam & Caffarella, 1991). Researchers thus concentrated on the ability of learners to execute steps in the process such as setting goals, identifying learning resources, developing strategies and evaluating results (Oddi, 1986). The attention here was on the level of learner autonomy over the instructional process (Song & Hill, 2007). Considering SDL as a process of self-instruction offers a very limited scope for a complex activity (Oddi, 1986). It does not account for most of the human learning which occurs. Knowles (1975) envisioned the self-directed learning as a six step process. These include climate setting; diagnosing learning needs; formulating learning goals; identifying resources for learning; choosing and implementing appropriate learning strategies; and evaluating learning outcomes. These conceptualizations were followed by recommendations to educators on how to facilitate self-directed learning. There are overlaps between Knowles’ and Tough’s descriptions of self-directed learning. Tough (1978) describes a framework which he used to investigate the process by which adults taught themselves. His work is considered to be the first comprehensive description of self-directed learning. He used the term adult learning projects to refer to self-teaching projects in which adults engaged in deliberate efforts to gain knowledge and skill or change in some way. To be included the learning efforts had to add up to a total seven hours. The learning efforts could take any form such as reading, listening, observing, reflecting, practicing, class attendance and getting answers to questions, if the primary purpose of that effort was to gain certain knowledge or skill including competence, habits, and attitudes among other changes. Tough’s investigations culminated in a linear model in which self-directed learners passed through thirteen steps in their learning projects. Each step in the model represents a key decision-making point in the learning process such as choosing what, where and when to learn 52 alongside deciding on resources to use for learning. He focused mainly on intentional learning, leaving out all activities in which learning was a by-product of some other task. Tough is credited with being among the first to clearly describe the self-directed learning process (Oddi, 1987). According to Oddi (1987) learning through the learning projects does not include all learning processes for adults; hence the model has a limited scope. Also the approach tends to view SDL as being episodic rather than as a dynamic process. Brockett and Hiemstra (1991) also see Tough’s work as only concerned with the planning and deciding portion of the learning process. Using qualitative methods, Roberson and Merriam (2005) investigated the self-directed learning efforts of older, rural adults. They came up with a series of events which constituted the process of self-directed learning for those older, rural adults. The process begins with an incentive to learn (in this case related to later life changes) which may be internal or external. The process of self-directed learning continues if there is an accompanying personal interest. The next step is accessing resources, a process which is as unique as the individual. Participants accessed several resources during their self-directed learning process. Systematic attention is the part of the process when the goals of self-directed learning become a priority. The next step is adjustments in learning activities, whereby obstacles, errors, or difficulty in the process are dealt with. The process may then come to a close at the resolution step or it may become an ongoing learning project. Also a catalyst was mentioned, which may be some event, which speeds up the process or motivates them to learn at a deeper level. Whereas being a process model, this model is more interactive than one proposed by Knowles (1975) and Tough (1978). It emphasizes more the role of adult development in the process of learning. According to Merriam and Caffarella (1991), learners use a variety of strategies when learning on their own. Learner choice introduces diversity in terms of the process of self-directed 53 learning making it difficult to conceptualize self-directed learning as being linear. After investigating adult learners (16 years or older) with less than high school completion who were engaged in a self-directed learning project, Spear and Mocker (1984) concluded that there was no evidence of detailed preplanning among the participants as had been emphasized by previous research. However, they concluded that such learning was deliberate, not random. Additionally, Oddi (1987) opined that linear conceptualizations of self-directed learning focus more on discrete episodes of learning but do not explain why such behaviors persist over time, nor do they explain interrelations between the episodes. The more recent self-directed learning models are more interactive, attributing self-directed learning to a combination of factors such as personality characteristics, cognitive processes, and context of learning (Merriam, Caffarella, & Baumgartner, 2007). Spear and Mocker (1984) developed a self-directed learning model whose emphasis was on the role life circumstances play in shaping one’s learning. The focus was on the triggering events that set the process in motion, how resources were acquired and how decisions regarding the learning process were made. They saw learners’ life circumstances as playing a major role in determining how self-directed learning starts and proceeds. Learners tended to organize and structure their learning projects depending on the circumstances within their environment. They labeled this phenomenon “the organizing circumstance”. In contrast with previous conceptualizations that had learners for example choosing from a variety of learning resources, they found that learners were more likely to use a single resource that was available fortuitously within their environment. After a triggering event, learners chose courses of action that occurred fortuitously in their environments (Ellinger, 2004). In their model, Spear and Mocker (2004) emphasized that personal knowledge, opportunities to learn and chance situations combine to shape a unique learning experience. They 54 proposed that a learning project begins with a triggering event, normally a change in life circumstances. The changed circumstance presents a single or very few opportunities or resources for learning which the adult can use. The structure, method, resources and conditions for learning are directed by the circumstances. The circumstances created in one discrete learning period become the circumstances for the next logical step. Brockett and Hiemstra (1991) developed the Personal Responsibility Orientation model which consists of two related dimensions; the instructional methods processes and the personality characteristics of the individual learner (learner self-direction). The instructional processes dimension concerns learners assuming responsibility for planning, implementing, and evaluating their learning. Educators here play a facilitating role. The learner self-direction centers on learners preference for assuming responsibility for learning. They combined both the external characteristics of the instructional process and the personal attributes or internal characteristics of the learner in the model. They acknowledge the importance of the social context in which self-directed learning occurs. Grow (1991) proposed the Staged Self-Directed Learning model (SSDL) which was designed with teachers, students and educational institutions in mind. The model proposes that learners advance through four stages of increasing self-direction. Teachers can aid or hinder that process. Self-directedness in learning can be taught and can be learned. The model offers a guide to teachers on how to help the students become more self-directed by individualizing their teaching style to match learners’ stage of self-direction. Good teaching varies in response to the learners, i.e. it is situational. The teachers’ role is to match the learners’ stage of self-direction and prepare the learners to advance to higher stages. Students can thus be moved from dependency to self-direction. The four stages in the Grow model are: 55  Stage one consists of dependent learners who need an authority-figure to give them specific directions on when, how and what to do. Here, learning is teacher-centered. Teaching should mainly be through coaching and insight  Stage two consists of learners of moderate self-direction. These are interested, “good students,” though they may be ignorant of the subject of instruction. The teacher here is called upon to be enthusiastic and motivating issuing highly supportive directives. Motivated and encouraged, students will continue to learn on their own. Students should begin to be trained in basic skills such as goal setting. Also tie the subject to the learner’s interest.  Stage three consists of learners of intermediate self-direction. The learners have skills and knowledge and can explore a subject with a good guide. The teacher should be a facilitator. Teachers and students share decision-making with students taking an increasingly important role.  Stage four consists of learners of high self-direction. These learners set their own goals or standards with or without the help of experts. They use experts, institutions and other resources to pursue these goals. The teacher here plays a delegating role and rather than teaching subject matter, he/she cultivates the student’s ability to learn. Grow (1991) offers a grid of 16 possible pairings between teaching styles and learning styles. The challenge is matching the teacher’s style to the learner’s degree of self-direction. Problems arise when such a match is lacking. Garrison’s (1997) model also incorporates the personal attributes as well as learning process perspectives of self-directed learning. It covers three dimensions, namely self management/external management (contextual control), self-monitoring/internal monitoring (cognitive responsibility), and motivational dimensions (entering and task). 56  Self management involves learners taking control of, and shaping, the contextual conditions to meet their learning goals. It is learners taking control of external activities associated with the learning process. This involves the enactment of learning goals and the management of learning resources. It is what learners do during the learning process.  Self-monitoring is the ability of learners to monitor their cognitive and meta-cognitive processes. It is the process by which learners monitor their own thinking, integrating new and existing knowledge structures, and modifying their thinking to meet learning goals. It is a process of constructing meaning. This process depends on internal and external feedback.  Garrison (1997) acknowledges that motivation plays a significant role in initiation and maintenance of effort towards learning. The motivational dimension involves what influences people to enter (entering motivation) and continue participating/persisting in a self-directed learning activity (task motivation). The model addresses self-directed learning in an educational context, which in itself is reducing a phenomenon that occurs in and out of educational settings. Self-directed learning can take place inside and outside the confines of formal education institutions (Ellinger, 2004). Also his focus on worthwhile learning as being socially negotiated may find support if the self-directed learning occurs in an educational setting. He mentions that “it is the teacher who can provide efficient and effective feedback for purposes of self-monitoring the quality (meaning and validity) of the learning outcome” (Garrison, 1997, p. 25). He argues that “absolute learner control may adversely affect or reduce the efficiency of achieving quality learning outcomes” (Garrison, 1997, p. 26) and also reduce learner persistence. However in a lifelong learning setting, it is assumed that the self-directed learner determines the learning needs and also 57 evaluates the learning. It is the learner who determines if the learning is complete and if it is worthwhile. Self-Directed Learning Readiness The assumption that learners become more self-directed and autonomous in adulthood led to studies which investigated self-directed learning as a personal characteristic or attribute (Merriam, Caffarella, & Baumgartner, 2007). According to Knowles (1990), as adults mature, they move towards self-direction, a sort of natural inclination. Grow (1991) also attributes self-direction as being partly a personal trait analogous to maturity, which once developed is transferable to new situations. Grow (1994) however sees self-direction, just like dependency, as something that can be learned and not something that comes with the state of being an adult. Two main instruments, the Oddi Continuing Learning Inventory (Oddi, 1986) and the Self-Directed Learning Readiness Scale (Guglielmino, 1978), have been used to study readiness to engage in self-directed learning. A recent one was developed by Fisher, King, and Tague (2001) for assessing nurses’ self-directed learning readiness. Oddi (1986) conceptualized self-directed learning as being broader than a self-instructional process. Her conceptualization considers an individual’s motivation to pursue and persist in learning throughout life rather than on the ability to engage in episodes of self-instruction. “…one need not be a proficient self-teacher in order to be a self-directed learner” (Oddi, 1987, p. 26). Thus she chose to use the term “self-directed continuing learning.” Consideration of self-directed learning as a self-instruction process fails to account for individuals whose learning styles are not compatible with planning courses of self-instruction. It also fails to account for persistence in learning (Oddi, 1987). She identifies persistence as a psychological variable not necessarily dependent on skill. 58 In developing the Oddi Continuing Learning Inventory (OCLI), Oddi (1986) focused on personality characteristics of individuals whose learning behavior is characterized by initiative and persistence in learning through a variety of modes. Focusing on personality characteristics provides a relatively stable indicator of Self-Directed Learning, one independent of the mode of learning (Oddi, 1987). The instrument was developed from an extensive list of personality characteristics derived from writings of experts on Self-Directed Learning and those variables supported by research findings. Those characteristics that were logically related were divided into groups which were refined to form three broad clusters (Oddi, 1986). These clusters were taken to be important dimensions with two poles: one having high amounts of that characteristic and the other low amounts of that characteristic. The conceptual clusters include (Oddi, 1986):  Proactive versus reactive drive (PD/RD): focuses on learners’ ability to initiate and persist in learning without obvious external reinforcement. On the one hand there is a learner who is self-confident, self-regulating who initiates and sustains learning activities. On the other hand there is a learner characterized as low on self-confidence, who relies on extrinsic forces to stimulate learning, and has a tendency to discontinue learning on encountering obstacles.  Cognitive openness versus defensiveness (CO/D): this dimension involves the consideration that openness to change is an important attribute of the self-directed learner. A high score represents openness to new ideas, ability to adapt to change and tolerance for ambiguity. The opposite pole represents attributes such as rigidity, fear of failure and avoidance of new ideas and activities.  Commitment to learning versus apathy or aversion to learning (CL/AAL): this is a dimension for people who enjoy learning for its own sake, participate in learning in a variety of modes, and learn on a continual basis. One pole involves expression of positive 59 attitudes towards engaging in learning activities of various sorts and a preference for thought-provoking leisure pursuits. The opposite pole includes expression of hostile attitudes towards engagement in learning activities. The three dimensions are assumed to be interrelated and mutually reinforcing. These three dimensions describe the motivational, cognitive and affective attributes of self-directed learners (Oddi, Ellis, & Roberson, 1990). The core dimensions of the theoretical formulations guided the construction of a 100 item pool. Content validation was achieved by subjecting the items to a review by panel of experts in the area of psychological constructs or adult education, and graduate students in law, nursing and adult education (Oddi, 1986). Further refinement was conducted through a pre-pilot and a pilot study. The resulting refined instrument was administered to a sample of 271 graduate students in law, nursing and adult education. The final OCLI instrument had 24 items and had a coefficient alpha of .875 and test-retest reliability of .893. Factor analysis of the 271 responses yielded three principle components accounting for 45.7% of the total variance. Rotation by oblimin technique yielded three interpretable factors (Oddi, 1986).  Factor I contained 15 salient items and accounted for 30.9% of the variance. It represented elements from the PD/RD dimension of the theoretical formulations. It gained additional items reflecting the ability to work independently and to learn through involvement with others. This was considered a general factor.  Factor II, Ability to be Self-Regulating, contained three salient items and accounted for 8% of the total variance. The items represented one of the elements in the PD/RD dimension of the theoretical formulations. 60  Factor III, Avidity for Reading, contained four salient items and accounted for 6.8% of the total variance. The items reflected a portion of the CL/AAL dimension. The failure of separate factors to explain adequate amounts of the total variance makes it necessary to use the total scores to assess validity of the instrument (Oddi, 1986). The ability to be a self-directed learner is related to neither intelligence nor intellectual achievement. The instrument measures aspects of an individual’s initiative and persistence in learning (Oddi, 1986). Based on correlations of the OCLI with other instruments of known reliability and validity, the instrument was found to be of satisfactory reliability and stability, when used in its entirety (Oddi, 1986). Its convergent validity was suggested by positive correlations between total OCLI scores and scores on the Leisure Activity Scale. Discriminant validity was demonstrated when the total OCLI scores failed to correlate significantly with scores on a measure of adult intelligence (Oddi, Ellis, & Roberson, 1990). Further construct validation tests were conducted by Oddi, Ellis, and Roberson (1990) aimed at examining the relationship between OCLI and certain behavioral characteristics thought to be indicative of SDL. Behaviors investigated include voluntary attendance and participation in job-related learning activities. Significant positive correlation found between OCLI scores and total Job Activity Survey (JAS) scores which measure participation in on the job learning activities suggested convergent validity, though the strength of the relationship was low. Also, there was failure of the OCLI scores to correlate highly with group instruction scores and voluntary attendance scores. The OCLI was also found to provide a better estimate of one’s ability to learn through self-instruction than through inquiry, performance and group instruction. In an exploratory factor analysis, Harvey, Rothman and Frecker (2006) found a three factor solution equivalent to Oddi’s (1986). However, a confirmatory factor analysis generated 61 four underlying dimensions of OCLI which include learning with others; learner motivation/autonomy/self-efficacy; ability to be self-regulating; and reading avidity. They found a four-factor solution to be offering a better fit. They called for further development and testing of the OCLI since all the models of the OCLI explained less than 50% of the variance in the analyzed response set. Oddi, Ellis, and Roberson (1990) called for a refinement of the OCLI to strengthen its ability to measure learning through different modes. According to Merriam, Caffarella, and Baumgartner (2007) more than twenty five variables have been correlated with OCLI scores. Some of the variables they cite include self-concept, on-the-job learning, left brain hemispherity, and grade-point average among others. The SDLRS is a 58 item, Likert scale instrument designed by Guglielmino in 1977 to assess an adult’s readiness for self-directed learning. According to Guglielmino (1978), self-directed learning readiness results from a complex of attitudes, values and abilities. The SDLRS was designed in two steps. First, fourteen experts in the field of self-directed learning were asked to name and rate characteristics they considered important for self-direction in learning including attitudes, abilities and personality characteristics. Characteristics that were rated as desirable or better were used to construct the SDLRS instrument. After some review and revision, an instrument was administered to 307 people in Georgia, Vermont and Canada. A reliability coefficient of .87 was estimated (Guglielmino, 1978). A factor analysis of the data revealed eight factors. They are self-concept as an effective learner; openness to learning; initiative and independence in learning; acceptance of responsibility for one’s learning; love for learning; creativity; future orientation; ability to use basic study and problem solving skills. The SDLRS is a measure of perceived readiness, not of actual self-directed learning behavior (Brockett, 1985b). It is the most frequently used quantitative measure of Self-Directed Learning. A large body of research supports the validity and reliability of the SDLRS 62 (Guglielmino, 1989). Merriam, Caffarella, and Baumgartner (2007) cite studies which have explored the relationship between self-directed readiness and such other measures as life satisfaction, job-satisfaction, and course grade, learning styles, cross-cultural adaptability and job performance. Self-directed learning readiness is a developable capacity in individuals (Guglielmino, 1989). Individuals who score low on SDLRS should be given opportunities to become effective self-directed learners (Merriam, Caffarella, & Baumgartner, 2007). Field (1989, 1990) raised concerns regarding the validity and reliability of the SDLRS. According to Field (1989) the scale has continued to be used under the assumption that its validity has been demonstrated. However the validity is questionable owing to the low to moderate association between the construct measured by this scale and other related constructs. He also disputed the current use of Guglielmino’s (1978) eight factor structure that was based on the original 41 item instrument, offering that the instrument has undergone changes including addition and deletion of items and a new factor structure was required. The eight-factor structure has proven difficult to replicate. Analysis of Field’s (1989) data showed that the construct being measured is homogenous and not related to self-directed learning readiness (Field, 1989). He called for a discontinuation in the use of the instrument, citing conceptual flaws (Field, 1990). Brockett’s (1985b) assertion that negatively phrased items were a significant source of invalidity found support in Field’s (1989) work. Guglielmino (1989) and Long (1989) criticized Field’s (1989) work on grounds of the statistical procedures he used and incorrect interpretation of the sources he cited. According to McCune (1989), Field’s analysis suggested some misunderstandings of the statistical concepts he employed. Brockett (1985b) encountered problems when administering the SDLRS to a group of older adults of low educational attainment and questioned the appropriateness of using the 63 instrument on that sample. He was concerned that the books and schooling orientation of SDLRS may not be appropriate for use on adults with relatively few years of formal schooling. Bonham’s (1991) investigation of the meaning of low scores on the SDLRS led her to question the construct validity of the instrument. Her conclusion was that low scores indicate a dislike for any kind of learning as opposed to other directedness, and hence construct validity may not be supported for the present meaning given to high scores. This may seem close to a conclusion that Field (1989) drew from his study, that the construct being measured by SDLRS is related to love of, and enthusiasm for, learning. Hassan (1981) vouched for the validity of the SDLRS after he found a significant predictable relationship between readiness for self-direction in learning and number of learning projects conducted in a year using Tough’s interview schedule. To address Brockett’s (1985b) concerns, a version for individuals with lower reading levels and lower levels of English proficiency was developed (Guglielmino, 1989). According to Brockett and Hiemstra (1991) the SDLRS’ contributions to the understanding of Self-Directed Learning outweighs its methodological concerns. They therefore do not advocate dismissing the instrument. Merriam, Caffarella and Baumgartner (2007) share the same sentiment. However the construct validity and reliability issues raised by, among others, Field (1989) are hard to ignore especially given the low item-to-total SDLRS score correlations and reliability problems when used with different populations. According to Fisher, King and Tague (2001) the degree of control learners take over their learning depends on their attitudes, abilities and personality characteristics. They developed a scale that assesses nursing students’ attitudes, abilities and personality characteristics for self-directed learning in a tertiary education setting. Whereas admitting that the SDLRS (Guglielmino, 1977) is the most widely used instrument in assessing self-directed learning 64 readiness, Fisher, King and Tague (2001) cite validity issues raised in the literature and cost issues related to the use of SDLRS as the main reasons warranting the construction of a new self-directed learning readiness instrument. Fisher, King and Tague (2001) developed a list of attitudes, abilities and personality characteristics of self-directed learners from an extensive literature review. A list of 93 items perceived to reflect self-directed learning readiness were presented to, and rated for relevance by a panel of 11 nursing education experts. After two rounds of the Delphi technique, a 52 item instrument was administered to a convenience sample of 201 undergraduate nursing students. Following an item-to-total correlation test, 10 items were dropped from the scale. Exploratory factor analysis was conducted on the remaining 42 items revealing three components labeled Self-Management, Desire for Learning and Self-Control. Two items that did not load on any of the three components were dropped from the scale. The Cronbach’s coefficient alpha for the total item pool (n=40) was 0.924, for the Self-Management subscale (n=13) was 0.857, the desire for learning subscale (n=12) was 0.847, and the Self-Control subscale (n=15) was 0.830. The resulting 40 item instrument was found to be homogenous and valid. A total score of more than 150 on the scale indicates readiness for SDL. The validity of this instrument was established by the development of the scale items from the literature, assessment by a panel of experts using a two-round Delphi technique and testing with exploratory factor analysis (Fisher, King & Tague, 2001, p. 520). Fisher, King and Tague (2001) called for more research to determine the validity of the instrument and confirm its factor structure especially across different racial groups. Smedley (2007) heeded the call by seeking to assess the validity and reliability of Fisher, King and Tague’s (2001) Self-Directed Learning Readiness Scale in another undergraduate nursing context. Her study sought to assess the self-directed learning readiness of beginning level 65 Bachelor of Nursing students at a college that offers some components of its program in the form of clinical learning logs and independent learning contracts (both forms of self-directed learning). Smedley (2007) conducted item-to-sum correlations which indicated that the instrument had significant internal consistency. Similarity between the Cronbach alpha coefficient computed for each of the three subscales by Fisher, King and Tague (2001) and Smedley (2007) reaffirmed the reliability and internal consistency of the instrument (Smedley, 2007). Rutledge (2006) used Fisher, King and Tague’s (2001) instrument to assess self-directed learning in graduate nursing students. She found the overall internal consistency as being good with a coefficient alpha .92. Thirty-six items had individual item-total correlations of more than .30. The three reverse-coded items had low correlations. Subscale alpha coefficients were acceptable. She concluded that the psychometrics of the SDLRS seemed adequate for use with graduate nursing students. Oddi, Ellis and Roberson (1990) called for further refinement of OCLI to strengthen its ability to measure learning through different modes. Harvey, Rothman and Frecker (2006) called for further development and testing of the OCLI since all the models of the OCLI explained less than 50% of the variance in the analyzed response set. Field (1989, 1990) raised concerns regarding the validity and reliability of the SDLRS developed by Guglielmino (1978). Items to assess self-directed learning readiness in this study were adapted from the self-directed learning readiness instrument developed by Fisher, King and Tague’s (2001). Readiness to Overcome Deterrents to Participation The issue of participation has been an issue of concern in the Adult Education field for many decades. Even in lifelong learning literature, there are calls for an understanding of non-participation and under-participation in learning opportunities. Richardson (1978) asserted that 66 federal lifelong policies should be directed at removing barriers to participation and towards developing learning opportunities. Tight (1998a) identified nonparticipation as a key problem in lifelong learning. Norman and Hyland (2003) called for a widening of participation, not just increasing participation. This, they said, would be done by removing obstacles for learners consistently under-represented in post-compulsory education. According to Valentine and Darkenwald (1990) helping adults overcome forces that keep them from participating in learning is the most difficult task confronting program planners in adult education. Early studies were focused on investigating the nature and extent of adult learning participation (Dickinson & Clark, 1975). Later studies aimed at understanding participation took two routes. Some studies investigated motivating factors such as Houle’s (1961), while others investigated factors that deter people from participating such as Beder, (1990), Darkenwald and Valentine, (1985), King, (2002), Martindale and Drake, (1989), and Scanlan and Darkenwald, (1984). In a study of adult motivations for participating in educational activities, Houle (1961) suggested that adult participants were either goal oriented (learn to accomplish a specific objective), activity oriented (learn to develop social contacts and relationships with others) or learning oriented (seek knowledge for its own sake), orientation in this case being the way adults viewed their involvement in learning. According to Dickinson and Clark (1975), Houle’s tripartite typology stimulated a lot of research which resulted in refinement of measurement techniques and extension of his three original categories. Dickinson and Clark (1975) examined the relationship between learning orientations and participation in different types of learning activities. The following table prepared by Dickinson and Clark (1975) shows how closely the results of other research on learning motivation approximated Houle’s tri-partite typology. 67 Table 1 Summary of Factors Identified in Studies of Learning Orientations Study HOULE TYPOLOGY Goal Orientation Activity Orientation Learning Orientation A. Continuing Learning Orientation Index (C.L.O.I.) 1. Sheffield Personal-goal Societal-goal Need fulfillment Sociability Learning 2. Sovie Persona-goal Occupational-goal Professional-goal Societal-goal Need fulfillment Personal-sociability Professional-sociability Learning B. Education Participation Scale (E.P.S.) 3. Boshier Other-directed advancement Social contact self vs. other-centeredness Educational Preparation 4. Morstain and Smart External expectations Professional advancement Social welfare Social relationships Escape/stimulation Cognitive interest C. Reasons for Educational Participation (R.E.P.) 5. Burgees Personal-goal Social-goal Religious-goal Meet formal requirements Social activity Escape Desire to know 6. Grabowski Personal-goal Social-goal Social activity Escape Study alone Desire to know Intellectual security Source: Dickinson and Clark (1975) Using results from his Education Participation Scale and borrowing from Maslow’s theory of hierarchy of needs Boshier (1977) distinguished between life-space (growth) participation motivation and life-chance (deficiency) participation motivation. Deficiency motivation caused people to participate in learning to remedy a deficiency or imbalance in one’s life. Participants here engage in learning to meet lower-order needs of survival. On the other 68 hand, growth motivation participation is part of self-actualizing behavior. Participants here have already met the lower-end needs in Maslow’s hierarchy of needs and are more concerned with expanding their life-space. He hypothesized that life-space participants will be more continuous in their learning than life-chance participants. Boshier and Collins (1985) conducted a large-scale empirical test of Houle’s tripartite typology. Whereas they found general support for Houle’s typology, they concluded participation behavior was more complex and finer distinctions were necessary. While the motivation studies offer invaluable insights into the participation phenomena, other researchers have looked to identify commonalities in deterrents/barriers to participating in educational activities (Beder, 1990; Darkenwald & Valentine, 1985; King, 2002; Martindale & Drake, 1989; Scanlan & Darkenwald, 1984). According to Scanlan and Darkenwald (1984) motivation studies were not successful in distinguishing between participants and non-participants. The terms barriers and deterrents have been used interchangeably in literature, but deterrents has come to gradually replace barriers. According to Valentine and Darkenwald (1990), the term barrier connotes an “absolute blockage, a static, insurmountable obstacle” (p. 30) while deterrents suggest a “more dynamic and less conclusive force … one that works in combination with other forces” (p. 30). Scanlan (1986) defines a deterrent to participation as a reason contributing to an adult’s decision not to engage in learning activities. Cross (1981) classified barriers to participation in learning activities under three groups: situational, institutional, and dispositional barriers. Situational are those reasons that arise from ones situation in life at a given time such as lack of time, lack of money, lack of child-care and transportation. Institutional barriers refer to practices and procedures that discourage adults from participating in educational activities. These are subconsciously erected by providers of 69 educational services. Examples include inconvenient schedules, inconvenient locations and inappropriate courses of study. Dispositional barriers refer to the attitudes and self-perceptions of learners which may inhibit participation in educational activities or educational attainment. For example older adults may feel they are too old to learn. Poor educational backgrounds or low grades in the past may engender a lack of interest in learning or low confidence in the ability to learn. In Table 2 below, Cross (1981) grouped barriers to learning identified by a national commissioned survey conducted by Carp, Peterson, and Roelfs (Cited in Cross, 1981) into situational, institutional, and dispositional barriers in order to illustrate the relative importance of the three types of barriers. Some barriers could fall under more than one category. For instance, lack of information could fall under the institutional and situational categories. Table 2 Perceived barriers to learning as identified by Carp, Peterson, and Roelfs (1974) and categorized by Cross (1981) Barriers Percent of Potential Learnersa Situational Barriers Cost including tuition, books, child care, and so on 53 Not enough time 46 Home responsibilities 32 Job responsibilities 28 No child care 11 No transportation 8 No place to study or practice 7 Friends or family don’t like the idea 3 Institutional Barriers Don’t want to go to school full time 35 Amount of time required to complete program 21 Courses aren’t scheduled when I can attend 16 No information about offerings 16 Strict attendance requirements 15 Courses I want don’t seem to be available 12 Too much red tape in getting enrolled 10 (Table Continued) 70 Don’t meet requirements to begin program 6 No way to get credit or a degree 5 Dispositional Barriers Afraid that I’m too old to begin 17 Low grades in past, not confident of my ability 12 Not enough energy or stamina 9 Don’t enjoy studying 9 Tired of School, tired of classrooms 6 Don’t know what to learn or what it would lead to 5 Hesitate to seem too ambitious 3 a Potential learners are those who indicated a desire to learn but who are not currently engaged in organized instruction. Source: Cross (1981), p. 99. According to Cross (1981) in all survey research situational barriers lead the list with the cost of education and lack of time leading all other barriers by substantial margins. When it comes to institutional barriers, learners complain the most about inconvenient locations and schedules, and lack of relevant or interesting courses. She contends that dispositional barriers have been underestimated in survey data due to the social desirability issue. People are likely to cite that they are busy rather than one lacks ability or is too old. Scanlan and Darkenwald (1984) were among the first to systematically investigate deterrents to participation. They developed the Deterrents to Participation Scale (DPS) and administered it to a large random sample of health professionals. They sought to explore an underlying meaningful structure to the many reasons adults offer for not participating in educational activities. A number of deterrents to participating in discipline specific educational courses were discovered. They fell under six factors which included disengagement, lack of quality, family constraints, cost, lack of benefit, and work constraints. This empirical study provided evidence to support the view that deterrents construct is multidimensional and way more complex than earlier conceptualizations suggested. The study was instrumental in the sense that it demonstrated empirically that deterrent factors could be identified and that they contributed to explaining variance in participation behavior. 71 Darkenwald and Valentine (1985) developed a more generic version of the DPS (DPS-G) that could be used to assess deterrents in the general population. They validated the instrument with the general adult population in the United States. An exploratory factor analysis revealed deterrents which included: lack of confidence; lack of course relevance; time constraints; low personal priority; cost; and personal problems. These were almost similar to the Scanlan and Darkenwald (1984) study. They suggested that the DPS-G be validated using different populations. The DPS-G was used in replication studies on enlisted U.S. Air Force personnel in two bases in Alabama (Martindale & Drake, 1989). The study revealed eight factors with the top six deterrents being the same in both groups. The deterrents include lack of course relevance, lack of confidence, cost, time constraints, lack of convenience, lack of interest, family problems and lack of encouragement. The responses aligned closely with those of Darkenwald and Valentine (1985). The differences were attributed to population differences in income, education and age (Martindale & Drake, 1989). The study established a general validity for the instrument. Blais, Duquette, and Painchaud (1989) investigated deterrents to participation in continuing nursing education to determine whether women working in a traditionally female profession were confronted with specific kinds of deterrents. They surveyed a relatively homogenous group of only diploma nurses who had not registered in any continuing educational activities outside their work in the past 12 months (pure non participants). An adjusted DPS instrument (Scanlan & Darkenwald, 1984) was used. This resulted in five factors, namely: incidental costs; conflicting role demands resulting from low priority for work-related educational activities; absence of external incentives; irrelevance of additional formal education for professional practice; and lack of information and affective support. Incidental costs and conflicting role demands resulting from low priority assigned to work-related educational 72 activities were identified as the most important barriers. They are similar in a generic way to cost and time constraints identified in previous research (Blais, Duquette & Painchaud, 1989). These above studies identified similar deterrent forces with the differences occurring being attributed to differences in the respective populations and methods of analysis. For example the top six deterrents in the Darkenwald and Valentine (1985) and Martindale and Drake (1989) studies were the same though not in the same order. Item means for the two groups were also observed to correlate closely. The consistency of the factors found in both studies supported the use of the instrument with other populations (Martindale & Drake, 1989). Drake (1988) used the DPS-G to investigate deterrents to agriculture teachers’ participation in credit and non-credit courses. He identified six factors, including lack of course relevance, cost, lack of confidence, time constraints and personal priority, lack of encouragement, and personal problems. These factors were similar to those identified by Darkenwald and Valentine (1985). While the above-mentioned studies among others helped identify deterrents to participation, they revealed little about the extent to which different types of potential learners experience these forces. Valentine and Darkenwald (1990) sought to present a typology of potential learners based on their self-reported deterrents. The main concern was how identified deterrent forces exhibited themselves among different populations. They administered the DPS-G to a sample derived from the general population and revealed five types. Type one were people deterred by personal problems (mostly homemakers); type two were largely deterred by lack of confidence; type three were deterred by educational costs; type four consisted of adults not interested in organized education; and type five were adults not interested in available courses. This study was important in revealing that deterrents to participation are likely to be differentially experienced by groups varyingly influenced by dispositional and situational factors. Adult populations differ substantially in, for example, age, race, gender, social-economic 73 status etc., which may have some effects on the potency of specific deterrents (Valentine &Darkenwald, 1990). Different groups experience unique combinations of barriers (Hayes, 1988); hence they may rate deterrents differently. This view seems to have driven the many population specific deterrents to participation studies. One such group was the low literacy level groups. Using a self-designed instrument, Beder (1990) investigated reasons for non participation in adult basic education (ABE) and found four factors: low perception of need; perceived effort (too much effort required); dislike for school; and situational barriers. These closely matched factors in Hayes’ (1988) study of low literate adults which used a special DPS-LL instrument. Five factors emerged including low self-confidence, social disapproval, situational barriers, negative attitudes towards classes, and low personal priority. These factors were similar to Beder’s (1990) except that Hayes (1988) identified low self-confidence and social disapproval as additional factors. King (2002) used the DPS to study barriers affecting GED participation among recent high school dropouts and identified nine factors. They included quality of course, perceived inability, time constraints, motivation, family constraints, logistical barriers, personal priorities, learning style, physical barriers. The highest rated factor was family constraint which included barriers such as lack of encouragement from family and friends, and reduction of time spent with family. Hayes (1988) argued correctly that low literate adults were not a homogenous group with regards to perceptions of barriers to participation. Results from these studies further supported the multidimensional nature of the deterrents construct especially since the deterrents to participation scale has resulted in differences in ratings of barriers even among homogenous groups. King (2002) called for analysis of deterrents to participation in different sub-groups in order to fully understand the deterrents construct. Johnson, Harrison, Burnett and Emerson (2003) investigated the deterrents to participation by adults in parenting education programs. The DPS-G was administered to a 74 mostly female sample. A factor analysis identified factors such as: lack of confidence; lack of course relevance; personal problems; situational barriers; and time. These findings were consistent with earlier uses of the instrument. Ballard and Morris (2005) used their own instrument to investigate the likelihood of midlife and older adults attending family life education programs and came up with four deterrents: programmatic deterrents; personal deterrents; time deterrents; and attendance deterrents. The older groups were more deterred by personal problems than younger ones. However, not all researchers used surveys to investigate deterrents. Cutz and Chandler (2000) used qualitative methods to study non-participation in adult education among the Maya of western Guatemala. They focused on understanding the emic reality (knowledge embedded and indigenous to a people, reproduced by indigenous people) as opposed to the etic reality (knowledge created by experts/researchers). The study revealed emic constructs at four levels (individual, family, community and national) which deterred participation. Most of the deterrents were related to issues of preservation of self-perception and identity as a Mayan male/female. Non-participation was seen as a defense to Mayan ethnic identity which was perceived to be destroyed by educational endeavors. Isaac and Rowland (2002) used qualitative methods to study the perceptions of African Americans of institutional barriers posed by their religious institutions which deter participation in their educational programs. Categories of barriers identified include lack of relevance; programmatic (no new programs); communication; interpretational; individual/personal; and instructional techniques. Religious educator was a unique deterrent identified. Several studies also sought to determine if any relationships existed between deterrents and various socio-demographic characteristics. Different relationships were identified for different populations. Beder’s (1990) study correlated low perception of need (e.g. it would not 75 improve my life) with variables such as widowhood, separation, divorce, full time employment among others. Johnson Harrison, Burnett and Emerson (2003) identified lower levels of education, as being associated with high importance on lack of confidence as a deterrent. Number of children correlated with a deterrent labeled time. Lower levels of family income were associated with higher rating of personal problems as a deterrent. These studies are significant contributors to deterrents research as they have investigated and presented in parsimonious ways the variables which deter participation in educational activities, identified specific typologies of potential learners, and helped determine the influence of demographic variables such as age, sex, income, and educational attainment on perception of deterrents. According to Darkenwald and Valentine (1985) deterrents do not work in isolation. The synergistic effects of multiple deterrents weigh in on the decision to participate in educational activities. Valentine and Darkenwald (1990) argue that adult population differences in say age, race, gender, and social-economic status may have some effects on the potency of specific deterrents. It is thus expected that different populations would rate variedly the importance of various deterrents in influencing their participation decisions. However, there are some deterrent factors that may be considered “universal” since they have been rated by respondents across different studies and populations as important in influencing their decision not to participate in learning activities. Cross (1981) reviewed research studies on deterrents to participation and found that situational barriers, in particular cost of education and lack of time, led all other barriers by substantial margins. Time and cost are the two most prevalent obstacles to enrolling in a course (Henry & Basile, 1994). Valentine and Darkenwald (1990) identified “time constraints” as a universal deterrent to participation. Other studies that have highlighted time constrains as a 76 deterrent factor include Beder (1990), Drake (1988), King (2002), Martindale and Drake (1989), Rubenson (2001) and Scanlan and Darkenwald (1984). Many studies have also identified cost as a deterrent factor including Darkenwald and Valentine (1985), Martindale and Drake (1989), Rubenson (2001) and Scanlan and Darkenwald (1984). In addition to time and cost issues, a literature review led to an identification of additional deterrent variables that are rated as important in influencing participation decisions across different study populations. These additional variables are based on items from the Deterrents to Participation Scale (Scanlan & Darkenwald, 1984) which is widely used in deterrent to participation studies. The variables met at least two criteria: they were items which had the highest loadings in the identified deterrent factors (at least 0.6 or above loading on a factor) and the variables had to be identified as important in several studies. They are listed below using Cross’s (1981) categorization of barriers as situational, institutional and dispositional. Institutional  Because the available courses did not seem useful or practical (Darkenwald & Valentine, 1985; Drake, 1988; Johnson et al. 2003; Martindale & Drake, 1989)  Because I didn’t think the course would meet my needs (Beder, 1990; Darkenwald & Valentine, 1985; Drake, 1988; Johnson et al. 2003; King, 2002; Martindale & Drake, 1989).  Because the courses available were of poor quality (Darkenwald & Valentine, 1985; Drake, 1988; Johnson et al. 2003; King, 2002; Scanlan & Darkenwald, 1984)  Because the course was scheduled at an inconvenient time (Darkenwald & Valentine, 1985; Drake, 1988; King, 2002; Martindale & Drake, 1989; Scanlan & Darkenwald, 1984) 77 Situational  Because I didn’t have the time for the studying required (Darkenwald &Valentine, 1985; Drake, 1988; Johnson et al. 2003; Martindale & Drake, 1989)  Because participation would take away from time with my family (Beder, 1990; Johnson et al. 2003; King, 2002; Martindale & Drake, 1989; Scanlan & Darkenwald, 1984)  Because I can’t afford the registration or course fees (Darkenwald &Valentine, 1985; Drake, 1988; Johnson et al. 2003; King, 2002; Martindale & Drake, 1989; Scanlan & Darkenwald, 1984)  Because I can’t afford miscellaneous expenses like travel, books etc (Darkenwald &Valentine, 1985; Drake, 1988; King, 2002; Martindale & Drake, 1989; Scanlan & Darkenwald, 1984) Dispositional  Because I was not confident of my learning ability (Darkenwald &Valentine, 1985; Drake, 1988; Johnson et al. 2003; Martindale & Drake, 1989; Scanlan & Darkenwald, 1984)  Because I felt I couldn’t compete with younger students (Darkenwald &Valentine, 1985; Johnson et al. 2003; King, 2002; Martindale & Drake, 1989)  Because I felt I was too old to take the course (Darkenwald &Valentine, 1985; Johnson et al. 2003; King, 2002; Martindale & Drake, 1989)  Because I felt unprepared for the course (Darkenwald &Valentine, 1985; Drake, 1988; Johnson et al. 2003; King, 2002; Martindale & Drake, 1989) Many variables deter participation in educational activities. The above variables have been rated as important in deterring participation in educational activities by the different populations studied. Learners are likely to encounter these deterrent variables, since they had the 78 highest ratings and cut across populations. Hassan (1981) found a negative significant correlation between the number of obstacles perceived by adult learners and their readiness for self-direction in learning. In his analysis of the AETS, Rubenson (2001) noted that when it comes to organized adult education, participants and non-participants mentioned situational barriers to about the same extent. It would not be hard to imagine that the difference between participants and non-participants in such a case would be the ability to overcome barriers to participation. Participants had to overcome those deterrents in order to participate. Any lifelong learner should have the capacity to overcome at least the deterrent factors identified above if they are to participate continuously in educational endeavors. Hence any conception of lifelong learning readiness should incorporate an assessment of readiness to overcome the above deterrents to participation in educational activities. Lifelong learners are more likely to self-identify as being able to overcome such deterrents since it is likely that they have had to overcome them before in their lifelong learning pursuits. Summary The rapid and pervasive nature of change witnessed since the 1960’s has necessitated the debates on the importance of lifelong learning at national levels. At the core of these debates is the issue of competitiveness for economies, organizations and individuals in the face of global competition and rapid technological change. Also discussed is the need for individuals to adapt to the increasing complexity of social and private life. Whereas the acknowledgement of the importance of lifelong learning is almost universal, the key challenge still is getting larger segments of the population to engage in lifelong learning. Participation statistics show that whereas adult participation in learning has increased over the years, there is still great opportunity for improvement not just for the general population, but also for groups that have continued to be underrepresented in adult learning activities. It is in recognition of the 79 participation problem that governments and educational institutions have commissioned studies on, and unveiled policy changes to widen participation in adult learning activities. However these have mainly focused on vocational-related learning and what institutions should do. Studies to understand lifelong learning from the learner’s perspective have also been undertaken. Among these are studies aimed at investigating the readiness of individuals to engage in lifelong learning. According to Marsick and Watkins (1997) people differ in their readiness for learning. Past studies on readiness for lifelong learning have either focused on career related learning (e.g. Hojat et. al., 2003) or they have used self-directed learning readiness as a measure of lifelong learning readiness (e.g. White, 2001). This study offers a broader conceptualization of readiness for lifelong learning as incorporating readiness to respond to triggers for learning, self-directed learning readiness, and readiness to overcome barriers to participation. 80 CHAPTER 3 METHODOLOGY Population and Sample The target population for this study was adults who volunteer to a 4-H youth development program. The accessible population was adult volunteers whose emails were available from the volunteer enrollment database system of a state 4-H Youth Development Program located in the Southern Region of the United States. The researcher obtained 2053 email addresses which represented the total number of volunteers of the state 4-H Youth Development Program who had already provided their email addresses in the volunteer enrollment database system. A total of 238 email addresses were erroneous or undeliverable. A final accessible population of 1815 volunteers, whose emails were usable, was targeted for this study. This study was considered a census (100% sample) of all those adult volunteers who had provided usable email addresses in the 4-H Youth Development Program volunteer enrollment database system. Ethical Considerations and Study Approval Prior to collecting data, an application for exemption from institutional oversight was submitted to the LSU Institutional Review Board. The study was granted approval # E4365 (Appendix A). Instrumentation An extensive review of the literature determined that no existing instrument entirely and satisfactorily demonstrated fidelity to the conceptualization of readiness for lifelong learning adopted for this study. Readiness for lifelong learning in this study is conceptualized as incorporating adults’ responses to triggers for learning, self-directed learning readiness and readiness to overcome deterrents to participation in learning. Therefore an instrument was 81 created with three sections: readiness to respond to triggers for learning, self-directed learning readiness, and readiness to overcome deterrents to participation in learning. Two sections of the questionnaire were created based on an extensive literature review and one section consisted of items drawn from an existing instrument. The instrument also contained a section designed to solicit the demographic information of the respondents. The first section contains items which assess the readiness to respond to triggers for learning (changes in life circumstances). Empirical studies by Aslanian and Brickell (1980a) and Tough (1979) identified changes in life circumstances rated highly by adults as triggering learning which fall within three broad areas of adult life: job and career; home and personal responsibilities; and leisure. A total of 29 items that represent changes in life circumstances in the three broad areas of adult life were developed for this section. Respondents were directed to rate the likelihood that they would seek and participate in learning activities when faced with those circumstances on a four point Likert-type scale: 1= very unlikely, 2= unlikely, 3= likely, and 4= very likely. The second section contains items which assess self-directed learning readiness. The items for this section were adapted from the Self-Directed Learning Readiness Scale (SDLRS) developed by Fisher, King and Tague (2001) which reflect the attributes, skills and motivational factors required of self directed learners. The SDLRS has 40 items and a reported Cronbach alpha .924. However, the SDLRS was designed to assess work-related self-directed learning readiness for nurses. Several changes were made to the adapted items to ensure they were in line with the broader conceptualization of lifelong learning taken in this study with regards to accommodating other reasons for learning, not just work-related, and other forms of learning such as informal learning. Items which had a work or performance emphasis were either rephrased or deleted. Negatively-phrased or reverse-coded items were rephrased. Rutledge 82 (2006) in a study of graduate nursing students’ self-directed learning readiness using the SDLRS developed by Fisher, King and Tague (2001) found that three reverse-coded items had low item-to-total correlations. Other studies, such as Brockett (1985b), using another SDLRS instrument (Guglielmino, 1977) found that negatively-phrased items were a source of invalidity. A total of thirty-two items were retained for this section. Respondents were directed to rate the degree to which each item measures a characteristic of themselves on a four-point Likert-type scale: 1= strongly disagree, 2= disagree, 3= agree, and 4= strongly agree. The third section contains items that measure the readiness to overcome deterrents to participation in learning. A literature review of deterrents to participation in learning studies led to an identification of deterrent variables that were rated as important in influencing participation decisions across different study populations. Most studies reviewed used various versions of the Deterrents to Participation Scale (Scanlan & Darkenwald, 1984), which is widely used in deterrent to participation studies. The variables selected met at least two criteria: they were items which had the highest loadings in the identified deterrent factors (at least 0.6 or above loading on a factor) and the variables had to be identified as important in several studies. Based on identified deterrents, 15 items were constructed for this section of the questionnaire to assess the respondents’ perception of overcoming those deterrents. Respondents were asked to rate their level of agreement with those statements on a four-point Likert-type scale: 1= strongly disagree, 2= disagree, 3= agree, and 4= strongly agree. The instrument was also used to collect demographic information. According to Desjardins, Rubenson, and Milana (2006) age, gender, formal education (highest educational level completed), socio-economic background (yearly net income), employment status, and race affect adult participation in learning. Other demographic information collected include: current occupation category; length in current employment; number of times the volunteer has changed 83 jobs in the past five years; whether or not volunteer’s current employment requires continuous certification; marital status; presence of children at home; length of time volunteering; and the format in which volunteer prefers learning. Questionnaire Pretesting The instrument was reviewed by three subject-matter (SME’s) experts to establish face and content validity. The SME’s have expertise in the following areas: adult education, social science research, volunteer development and 4-H youth development. Appropriate revisions were made to the instrument based on the input of each SME with regards to the necessity, relevance, structure and clarity of each question and instructions. Twenty graduate students attending a doctoral level Research Methods class in the School of Human Resource Education and Workforce Development (SHREWD) were asked to respond to the questionnaire and offer feedback as to the necessity, relevance, structure, and clarity of each of the questions and instructions. They also offered feedback on the length and overall ease in completing the questionnaire. Most of the students were in the Adult Education, Human Resource Development or Agricultural Extension concentration area within the SHREWD. The feedback of these students was useful since they were in an advanced research methods class, and their specialization areas exposed them to an understanding of adult learning principles. Finally, 15 members of a church were requested to respond to the questionnaire. Feedback on issues such a readability, clarity, amount of time taken to complete the survey, and overall ease in completing the survey were solicited. These members views were meant to approximate the adult volunteers of the 4-H Youth Development program that were the target of this survey. 84 Based on the feedback received, appropriate revisions were made to the questionnaire. Of the many revisions made to the questionnaire, the biggest change that was made was on the scale for the “Readiness to Respond to Triggers for Learning” section of the questionnaire. Initially, respondents were directed to rate the likelihood that they would seek and participate in learning activities when faced with certain circumstances believed to triggers adult engagement with learning on a four-point Likert-type scale: 1= very unlikely, 2= unlikely, 3= likely and 4= very likely. Many respondents in the pre-testing stage identified that some circumstances that were listed would not be applicable to them. Hence, the researcher included “not-applicable” in the scale to cater to that need. Hence the scale for the “Readiness to Respond to Triggers for Learning” section was a five-point Likert-type scale: 1= very unlikely, 2= unlikely, 3= likely and 4= very likely, 5= not-applicable. As for the Self-Directed Learning Readiness and Readiness to Overcome Deterrents to Participation sections, the four-point Likert-type scale: 1= strongly disagree, 2= disagree, 3= agree, and 4= strongly agree was retained. The Readiness for Lifelong Learning questionnaire consists of three sections: Readiness to Respond to Triggers for Learning; Readiness to overcome deterrents to Learning; and Self-Directed Learning Readiness. The first two sections were developed by the researcher, while the last section was adapted and modified from an existing questionnaire. Since two sections of this survey were new, and one section had been modified from an existing questionnaire, Factor Analysis was undertaken to examine the structure of interrelationships between the variables generated by this questionnaire to reveal any underlying dimensions or factors. In this case, Exploratory Factor Analysis (EFA) was performed on the data generated from this study to examine whether there were any underlying factors measured by the instrument based on the inter-correlations between the variables. These underlying factors are supposed to be fewer in number but still parsimoniously represent the original set of 85 observations. The EFA is useful in searching for structure among a set of variables without setting a priori constrains on the estimation of components or the number of components to be extracted. This method is appropriate when the researcher has no pre-conceived thoughts on the actual structure of the data (Hair, Anderson, Tatham & Black, 1998), as is the case when a researcher has a newly developed questionnaire. EFA was conducted for each of the three sections that comprise the Readiness for Lifelong Learning Survey. Before conducting EFA, a few tests were administered for each scale to determine whether the data were appropriate for Factor Analysis. They include a visual exam of the correlation matrix, Bartlett’s Test of Sphericity, and the Kaiser-Meyer-Olkin (KMO) Measure of Sampling Adequacy. The correlation matrix summarizes the interrelationships among the items or variables in the scale (Pett, Lackey, & Sullivan, 2003). The correlations range from -1.0 to +1.0, with values closer to one in either direction indicating a stronger positive or negative relationship between variables. The first step was a visual inspection of the correlation matrix for each of the three sections to see if they had sufficient correlations to support a factor analysis. Hair, Anderson, Tatham and Black (1998) suggest that the correlation matrix should have a substantial number of correlations greater that .30. The Bartlett’s Test of Sphericity was also conducted for each of the three scales that form the Readiness for Lifelong Learning Scale. The test provides a statistical probability that the correlation matrix has significant correlations among some of the variables (Hair, Anderson, Tatham, & Black, 1998). It tests the null hypothesis that the correlation matrix is an identity matrix (Pett, Lackey, & Sullivan, 2003). The null hypothesis is rejected with larger values of the Bartlett’s test. Finally, Kaiser-Meyer-Olkin (KMO) Measure of Sampling Adequacy was undertaken. Kaiser (as cited in Pett, Lackey, & Sullivan, 2003) suggests a value of 0.60 as a minimum score for the KMO. 86 After the tests, the factor structure of each of the three scales was explored by means of a Common Factor Analysis (CFA) which derives factors based on common variance. According to Hair, Anderson, Tatham and Black (1998) CFA is most appropriate “when the primary objective is to identify latent dimensions or constructs represented in the original variables, and the researcher has little knowledge about the amount of specific and error variance and therefore wishes to eliminate this variance…” (p. 102). Principal Axis Factoring extraction was deemed appropriate for this study and a Promax (Oblique) rotation was used to obtain a simpler factor structure. According to Hair, Anderson, Tatham and Black (1998) researchers seldom use a single criterion to determine how many factors to extract. The number of underlying factors to extract was jointly determined by the Kaiser Criteria (factors with an eigenvalue greater than 1), percentage of variance criterion (percentage of total variance extracted by successive factors > 5%), and by examination of the Cattell Scree plot. Factor loadings greater than +/- 0.30 met the minimum criteria to be considered for interpretation. In addition to using the above guidelines in determining the factors to extract, the practicality and interpretability of the factors affected the final factor selection at which the researcher arrived. Data Collection The survey was administered via an online survey system (Zoomerang). The online survey system was considered economical and convenient especially since the state 4-H Youth Development organization used in this study had a enrollment database for volunteers complete with their email addresses. All those adult volunteers who had provided usable email addresses in the 4-H Youth Development Program volunteer enrollment database system were surveyed in this study.
87 According to Dillman (2007), multiple contacts are essential for maximizing response to surveys. He suggests at least five contacts with respondents. A total of seven contacts with respondents were undertaken in this study, with the two additional contacts being undertaken to mitigate a low response rate. The following process was undertaken to collect the data: 1. Two days prior to administering the survey, a brief letter was sent via email notifying respondents of the upcoming study, its importance and requesting their participation. 2. The web-based questionnaire was emailed two days after the pre-survey notification. The email consisted of an electronic cover letter requesting the respondents’ participation and providing instructions for completing the survey including the url-link leading to the survey. All respondents who preferred to respond to a hard-copy questionnaire were asked to provide their physical address via a provided email address, and they would be mailed a questionnaire. 3. One week after sending the email with the url-link, all non-respondents were sent a friendly email reminder with an URL-link to the survey. 4. Two weeks following the email reminder, all non-respondents were sent another email, stressing the importance of their participation and a url-link to the survey. 5. One week later, non-respondents were sent a reminder stressing the importance of the study and a url-link leading to the survey. 6. In an effort to increase the response rate, an additional reminder was sent out one week later. The reminder stressed the importance of the study and it also provided the url-link to the survey. 7. The researcher requested the Volunteer and Leader Specialist with the state 4-H Youth Development organization used in this study to send the last reminder, encouraging the respondents to participate in this study. This was in an effort to increase the response rate.
88 A total of 320 respondents completed the web-based questionnaire and 4 respondents completed the hard-copy questionnaire. A careful examination of the responses revealed cases where some respondents had completed the web-based questionnaire more than once. It was discovered that some volunteers were enrolled more than once in the volunteer enrollment database, in some cases providing different email addresses. All responses were carefully examined to eliminate cases of double responses. In cases where a respondent was found to have responded twice to the questionnaire, the response with the most completed number of questions was kept while the other one was deleted. In cases where a respondent was found to have responded twice to the questionnaire, and both responses were complete, the first response was retained. The final response count was 277 responses out of a possible 1815 respondent (15.3% response rate). In order to determine if there were any statistically significant differences between respondents and non-respondents, a comparison was made between the overall mean score of early respondents and that of late respondents. Statistically significant differences were not found between early and late respondents, and it was thus concluded that no statistically significant differences existed between the respondent and non-respondents in this study. Data Analysis Below is a description of how data collected will be analyzed for each objective: Objective One Objective one was descriptive in nature and was analyzed using descriptive statistical techniques. Adult volunteers affiliated with a 4-H Youth Development Program in the southern region of the United States were described on the following variables: age, gender, ethnicity, highest level of education completed, yearly net income, marital status, presence of children at home, employment status, length in current employment position, current occupational category,
89 whether or not volunteer’s current employment requires continuous certification/licensure, number of times respondent has changed jobs in the last five years, length of time volunteering, and the format in which respondents prefer learning. The above demographic variables were summarized using frequencies and percentages in each category. Additionally, means and standard deviations of the interval variables age, number of times the volunteer has changed jobs in the past five years, and length of time volunteering were calculated. Objective Two Objective two was to determine the readiness for lifelong learning of adult volunteers affiliated with a 4-H Youth Development Program in the southern region of the United States as measured by the Readiness for Lifelong Learning Scale. First, exploratory factor analysis was conducted for each of the three sections of the scale with the aim of uncovering the structure of interrelationships of the variables in the scale and defining a common set of underlying dimensions or factors. Principal axis factoring extraction with promax oblique rotation was utilized. Factors with eigenvalues greater than 1 were retained for interpretation. Each respondent’s level of readiness for lifelong learning was determined by a summation of the sub-scale scores of the three sections of the Readiness for Lifelong Learning Survey. The objective was descriptive in nature and was analyzed through the calculation of means and standard deviations of the summated scores. Objective Three Objective three was to determine whether differences exist in the readiness for lifelong learning as measured by the Readiness for Lifelong Learning Scale on selected demographic characteristics which include: a) Gender
90 b) Ethnicity c) Highest educational level completed d) Yearly net income e) Marital status f) Presence of children at home g) Employment status h) Current occupational category i) Whether or not volunteer’s current employment requires continuous certification j) Format in which respondents prefer learning The objective was accomplished through the analysis of Independent t-tests and One-way Analysis of Variance. Levene’s Test was used to examine the homogeneity of variance. The interval variable overall readiness for lifelong learning was determined by the summation of the sub-scale scores from the three sections that comprised the readiness for lifelong learning survey. The overall readiness for lifelong learning item mean score was compared among the groups or levels within the above demographic variables. Objective Four Objective four is to determine whether a model exists which would explain a significant portion of the variance of readiness for lifelong learning as measured by the Readiness for Lifelong Learning Survey from the subscales or latent factors and associated variables that emerge statistically following a factor analysis of the dataset, and the demographic characteristics of age, gender, income, highest educational level completed and employment status. Objective four will be accomplished through multiple regression analysis. The sum of items emerging as indicators of the latent constructs will be calculated to represent the dependent
91 variables. Demographic variables age, gender, income, highest educational level and employment status will be entered stepwise into the equation as a block owing to the exploratory nature of the study. There will be as many separate multiple equations as there will be sub-scales that emerge statistically after factor analysis of the collected data.
92 CHAPTER 4 RESULTS The primary purpose of this study was to explore and determine the readiness for lifelong learning of volunteers affiliated with a 4-H Youth Development program in the southern region of the United States. The results of this study organized around four objectives are presented in this chapter. Objective One Objective one was to describe adult volunteers affiliated with a 4-H Youth Development Program in the southern region of the United States on the following demographic characteristics: a) Age b) Gender c) Ethnicity d) Highest educational level completed e) Yearly net income f) Marital Status g) Presence of children at home h) Employment status i) Length in current employment position j) Current occupational category k) Whether or not volunteer’s current employment requires continuous certification l) Number of times respondent has changed jobs in the last five years m) Length of time volunteering n) Format in which respondents prefer learning
93 Age Participants were asked to provide their actual ages, which were then grouped into the following categories: 1) 18-25; 2) 26-35; 3) 36-45; 4) 46-55; 5) 55-65; 6) 65 and above. The ages ranged from 19 to 75 years. The largest group of respondents indicated their age fell between 36 and 45 years (n = 96, 35%). The second largest group indicated their age fell between 46 and 55 years (n = 91, 33.2%). Table 3 illustrates the distribution of age of respondents. Table 3 Age Distribution of Adult Volunteers Affiliated with a 4-H Youth Development Program in the Southern Region of the United States Age in Years na Percentage 36-45 96 35.0 46-55 91 33.2 56-65 38 13.9 26-35 36 13.1 18-25 7 2.6 65 and above 6 2.2 Total 274 100.0 Note: Three respondents failed to respond to the age item on the questionnaire. a M = 46.05; SD = 11.46 Gender The study participants were also described on gender. Majority of the respondents indicated their gender as female (n = 230, 83.9%) while 44 respondents (16.1%) indicated their gender as male. Three respondents failed to indicate their gender. Ethnicity The respondents were further described on the ethnicity variable. Majority of the respondents identified themselves as Caucasians (n = 238, 87.2%). The second largest group identified themselves as African American (n = 28, 10.3%). Table 4 illustrates data regarding the ethnicity of the respondents.
94 Table 4 Self-Identified Ethnicity of Adult Volunteers Affiliated with a 4-H Youth Development Program in the Southern Region of the United States Ethnicity N Percentagea Caucasian 238 87.2 African American 28 10.3 Asian 1 0.4 Hispanic 1 0.4 Native American 1 0.4 Other 4 1.5 Total 273 100.0 Note: Four respondents failed to respond to the ethnicity item on the questionnaire a Total rounded to 100.0% Highest Level of Education Completed Regarding the highest level of education completed, the largest group of the respondents (n = 117, 43.0%) reported completion of a Bachelor of Arts or Science degree. The second largest group (n = 82, 30.1%) reported Masters Degree as the highest level of education completed. Three respondents (1.1%) reported a doctorate as the highest level of education completed. Table 5 illustrates data regarding the highest level of education completed by the respondents. Table 5 Highest Level of Education Completed by Adult Volunteers Affiliated with a 4-H Youth Development Program in the Southern Region of the United States Level of Education n Percentagea Bachelors Degree (BA/BS) 117 43.0 Masters Degree (MA/MS/MBA) 82 30.1 Some College 26 9.6 High School Diploma 14 5.1 Associate Degree 13 4.8 Vocational/Technical School Degree 8 2.9 Some Vocational/Technical School 5 1.8 Professional Degree (J.D./M.D.) 4 1.5 Doctoral Degree (Ph.D./Ed.D./Psy.D) 3 1.1 Less than High School 0 0.0 Total 272 100.0 Note: Five respondents failed to respond to the highest level of education item on the questionnaire a Total rounded to 100.0%
95 Yearly Net Income On their yearly net incomes, the largest number of respondents (n = 107, 41.3%) reported that their yearly net incomes fell between $25,000 and $50,000. The smallest number of respondents (n = 19, 7.3%) reported that their net yearly income was above 100,000. Table 6 illustrates data regarding yearly net incomes of survey participants. Table 6 Yearly Net Incomes as Reported by Adult Volunteers Affiliated with a 4-H Youth Development Program in the Southern Region of the United States Income Range in United States Dollars N Percentagea 25,001-50,000 107 41.3 50,001-75,000 70 27.0 75,001-100,000 35 13.5 Less than 25,000 28 10.8 Greater than 100,000 19 7.3 Total 259 100.0 Note: Eighteen respondents failed to respond to the yearly net income item on the questionnaire a Total rounded to 100.0% Marital Status Respondents were also asked to indicate their marital status. Of all the respondents majority reported being married (n = 214, 79.3%). The second largest group reported being single/never married (n = 26, 9.6%). The group featuring the least number of respondents was widowed (n = 8, 3.0%). Table 7 illustrates the marital status data for the respondents. Table 7 Marital Status Reported by Adult Volunteers Affiliated with a 4-H Youth Development Program in the Southern Region of the United States Marital Status N Percentage Married 214 79.3 Single/Never Married 26 9.6 Divorced 20 7.4 Widowed 8 3.0 Separated 2 0.7 Total 270 100.0 Note: Seven respondents failed to respond to the marital status item on the questionnaire
96 Presence of Children at Home Respondents were also asked if they had any children at home. The largest group of respondents (n = 184, 67.9%) reported having children at home. Eighty-seven respondents (32.1%) indicated that they do not have children at home. Six respondents failed to provide a response to this question. Employment Status Respondents additionally provided information about their current employment status. Majority of the respondents reported being employed full time (n = 226, 82.5%). The categories with the lowest number of respondents were “Employed on a contract basis” (n = 9, 3.3%) and “Unemployed” (n = 10, 3.6%). Table 8 illustrates information about respondent’s employment status. Table 8 Current Employment Status as Reported by Adult Volunteers Affiliated with a 4-H Youth Development Program in the Southern Region of the United States Employment Status N Percentage Employed Full Time 226 82.5 Employed Part Time 17 6.2 Retired 12 4.4 Unemployed 10 3.6 Employed on a Contract Basis 9 3.3 Total 274 100.0 Note: Three respondents failed to respond to the employment status item Length in Current Employment Position Study participants were invited to indicate how long they have been employed in their current position. Respondents provided their actual individual length of time in their current employment which the researcher subsequently grouped into categories (See Table 9). The largest group of respondents (n = 111, 43.4%) reported being in their current employment for between 1 and 10 years. Sixty-six respondents (25.8%) reported being in their current employment for between 10 and 20 years.
97 Table 9 Length in Current Employment Reported by Adult Volunteers Affiliated with a 4-H Youth Development Program in the Southern Region of the United States Length in Current Employment na Percentageb 1.01 – 10 years 111 43.4 10.01 – 20 years 66 25.8 20.01 – 30 years 42 16.4 30.01 – 40 years 22 8.6 One year or less 12 4.7 0 Years (Not currently employed) 3 1.2 Total 256 100.0 Note: Twenty one respondents failed to respond to the length in current employment item on the questionnaire a M = 19.85; SD = 24.72 b Total rounded to 100.0% Current Occupational Category Respondents were presented with three occupational categories and asked to select the one category that described their current occupation. Examples of occupations within each category were provided to aid respondents in their choice (See Appendix B). The majority of the respondents (n = 217, 88.9%) categorized their occupation as professional/managerial. Table 10 illustrates the current occupational category of the respondents. Table 10 Current Occupational Categories of Adult Volunteers Affiliated with a 4-H Youth Development Program in the Southern Region of the United States Current Occupational Category N Percentage Professional/Managerial 217 88.9 Sales/Service/Support 22 9.0 Trade/Labor 5 2.1 Total 244 100.0 Note: Thirty three respondents failed to respond to the current Occupational category item on the questionnaire Current Employment Requires Continuous Certification/Licensure A total of 175 respondents (66.8%) indicated that their current employment requires continuous certification or licensure. The remaining 87 respondents (33.2%) indicated that their
98 current employment did not require continuous certification. Fifteen respondents failed to respond to this questionnaire item. Number of Times Respondent Has Changed Jobs in the Last Five Years Respondent were additionally asked to indicate how many times they had changed jobs in the last five years. Majority (n = 191, 72.6%) indicated they had not changed jobs in the last five years. About 10 respondents (3.8%) indicated having changed jobs more than 3 times. Table 11 illustrates responses to the above question item. Table 11 Number of Times Adult Volunteers Affiliated with a 4-H Youth Development Program in the Southern Region of the United States Changed Jobs in the Last Five Years Number of times na Percentageb 0 191 73.2 1 47 18.0 2 14 5.4 3 5 2.2 5 3 1.9 6 1 0.4 Total 261 100.0 Note: Sixteen respondents failed to respond to this item on the questionnaire a M = 6.12; SD = 23.06; b Total rounded to 100.0% Length of Time Volunteering Respondents were asked to indicate how long they had volunteered with the 4-H Youth Development Organization. Each respondent provided the length of time they had been volunteering, which was then placed in categories (See Table 12). About 165 respondents (65.2%) reported having volunteered for between 1 and 10 years. Table 12 Length of Time the Adult Volunteers Report Having Volunteered with the 4-H Youth Development Program in the Southern Region of the United States Length of Time Volunteering na Percentage 1.01 – 10 years 165 65.2 10.01 – 20 years 42 16.6 One year or less 21 8.3 (Table continued)
99 20.01 – 30 years 15 5.9 30.01 – 40 years 6 2.4 0 Years (Never volunteered) 3 1.2 40.01 – 50 years 1 0.4 Total 253 100.0 Note: Twenty four respondents failed to respond to the item for length of time volunteering a M = 16.06; SD = 26.77 Format in Which Respondents Prefer Learning Of the various formats for learning, respondents were directed to indicate preference for one format. One hundred and fifty four respondents ( 59.5%) reported preference for workshops (See Table 13). The least preferred format for learning was mail correspondence (n = 8, 3.1%). Table 13 Preference for a Format for Learning Expressed by Adult Volunteers Affiliated with the 4-H Youth Development Program in the Southern Region of the United States Preference for a Format for Learning na Percentage Workshops 154 59.5 Web-Based/Online Training 69 26.6 Formal Classes 14 5.4 Mentoring 14 5.4 Mail Correspondence 8 3.1 Total 259 100.0 Note: Eighteen respondents failed to respond to the item for preference for a learning format Objective Two Objective two was to determine the readiness for lifelong learning of adult volunteers affiliated with a 4-H Youth Development Program in the southern region of the United States as measured by the Readiness for Lifelong Learning Scale. Each respondent’s level of readiness for lifelong learning score was determined by a summation of the sub-scale scores of the three sections of the Readiness for Lifelong Learning Survey. Therefore each individual’s readiness for lifelong learning score was obtained summing up the individual’s summated scores from each of the three sections of the survey. The objective was descriptive in nature and was analyzed through the calculation of means and standard deviations of the summated scores.
100 Each of the three sections of the Readiness for Lifelong Learning survey was first individually subjected to factor analytic procedures to investigate their underlying dimensions or factors based on the interrelationships of the variables in the scales. Self-Directed Learning Readiness Respondents were presented with a list of characteristics related to self-directed learning readiness and were asked to rate the extent to which each item measured a characteristic of themselves on a four-point Likert-type scale: 1= strongly disagree, 2= disagree, 3= agree, and 4= strongly agree. The following scale was created by the researcher to aid in the interpretation of the responses: 1 – 1.75= strongly disagree, 1.76 – 2.50= disagree, 2.51 – 3.25= agree, and 3.26 – 4.00= strongly agree. As part of the analysis, the means and standard deviations of the responses to each item in the Self-Directed Learning Readiness (SDL) part of the survey was calculated. The item that received the highest level of agreement from respondents was “I have high personal Standards” with a mean 3.67 (SD= 0.47). The item that received the second highest level of agreement from respondents was “I am responsible” with a mean of 3.66 (SD= 0.48). Using the interpretive scale, both were in the “strongly agree” range. The item with the lowest level of agreement was “I set specific times for my study” with a mean of 2.57 (SD= 0.65). The item with the second lowest level of agreement was “I prefer to set my own criteria on which to evaluate my learning” with a mean of 2.92 (SD= 0.59). The response to both items fell within the “agree” range. Overall, the response to most items (27 items) fell within the “agree” range on the interpretive scale. Table 14 below illustrates the mean scores and standard deviation for each item representing respondents’ levels of agreement with self-directed learning (SDL) characteristics.
101 Table 14 Description of the Level of Agreement of Adult Volunteers Affiliated with the 4-H Youth Development Program in the Southern Region of the United States with Statements Reflecting Self-Directed Learning Readiness Characteristics Self-Directed Learning Readiness Items Ma SD Categoryb SDL14. I have high personal standards 3.67 .471 SA SDL11. I am responsible 3.66 .476 SA SDL28. I am responsible for my decisions/actions 3.64 .480 SA SDL13. I have high personal expectations 3.62 .523 SA SDL31. I like to make decisions for myself 3.50 .527 SA SDL8. I learn from my mistakes 3.48 .527 A SDL22. I enjoy learning new information 3.43 .531 SA SDL17. I am confident in my ability to search out information 3.43 .530 SA SDL9. I am open to new ideas 3.43 .543 A SDL21. I want to learn new information 3.42 .499 SA SDL10. When presented with a problem I cannot resolve, I will ask for assistance 3.41 .561 SA SDL30. I can find out information for myself 3.41 .524 SA SDL6. I need to know why 3.38 .591 SA SDL15. I have high beliefs in my abilities 3.38 .569 SA SDL29. I can be trusted to pursue my own learning 3.34 .550 SA SDL5. I am able to focus on a problem 3.33 .561 SA SDL25. I like to gather facts before I make a decision 3.33 .505 SA SDL19. I have a need to learn 3.31 .572 SA SDL12. I like to evaluate what I do 3.30 .579 SA SDL20. I enjoy a challenge 3.30 .557 SA SDL16. I am aware of my own limitations 3.26 .540 SA SDL1. I solve problems using a plan 3.22 .557 A SDL32. I prefer to set my own learning goals 3.21 .553 A SDL3. I have good management skills 3.17 .548 A SDL7. I critically evaluate new ideas 3.12 .593 A SDL26. I evaluate my own learning 3.09 .510 A SDL4. I prefer to plan my own learning 3.03 .616 A SDL2. I manage my time well 3.00 .608 A SDL24. I am self-disciplined 3.00 .611 A SDL18. I enjoy studying 2.92 .700 A SDL27. I prefer to set my own criteria on which to evaluate my learning 2.92 .594 A SDL23. I set specific times for my study 2.57 .647 A Note: N= 277. Missing values replaced with variable mean a Response scale: 1 = strongly disagree (SD), 2 = disagree (D), 3 = agree (A), and 4 = strongly agree (SA) b Interpretive scale: 1 – 1.75= SD, 1.76 – 2.50= D, 2.51 – 3.25= A, and 3.26 – 4.00= SA
102 Factor analysis procedures were used to investigate the underlying correlation structure of the variables in this scale. Several tests were undertaken to examine whether the data was factorable. A visual inspection of the correlation matrix showed that a substantial number of correlations were greater than 0.30. The Bartlett’s Test of Sphericity was found to be acceptable (3534.08; df= 496; p < .001). Finally, Kaiser-Meyer-Olkin (KMO) Measure of Sampling Adequacy test returned an acceptable score of 0.905. The data was thus deemed factorable. Common Factor Analysis with Principal Axis Factoring extraction was undertaken on the data. Promax (Oblique) rotation with Kaiser Normalization was undertaken to obtain a simpler, more interpretable factor structure. In determining the number of underlying factors to be extracted the researcher considered the Kaiser Criteria (factors with an eigenvalue greater than 1), percentage of variance criterion (percentage of total variance extracted by successive factors > 5%), and the Cattell Scree plot examination. Factor loadings greater than +/- 0.30 met the minimum criteria to be considered for interpretation. In addition to using the above guidelines in determining the factors to extract, the simplicity, practicality and interpretability of the factors affected the final factor solution that this researcher reached for the Self-Directed Learning Readiness Scale. The initial factor analysis yielded four factors with eigenvalues greater than 1.0 which explained 40.61% of the total variance. An examination of the scree plot indicated at least five factors, with a substantial drop in the first factor followed by small drops in the remaining four factors. Since only four factors met the eigenvalue criterion, and there was a very small drop between the third and fourth factor in the scree plot, this was deemed a four factor solution. However, an examination of the factor matrix indicated that all items loaded strongly on Factor One with item loadings ranging from .36 to .69. The items that loaded on Factor Two, Factor Three and Factor Four were all cross-loading on Factor One, with the stronger numerical values loading on Factor One. This tends to indicate the presence of one strong factor, an observation
103 supported by the substantial drop in the first factor on the scree plot. Furthermore, only the First Factor met the percentage of variance criterion (percentage of total variance extracted by successive factors > 5%). An assessment of a forced Three-Factor solution and a Two-Factor solution yielded the same conclusion. The Three-Factor solution explained 37.139% of the total variance. The scree plot indicated a substantial drop in the First Factor followed by small drops in the subsequent three factors. The factor matrix revealed that all items loaded strongly on Factor One with item loadings ranging from .36 to .69. Only the First Factor met the percentage of variance criterion (percentage of total variance extracted by successive factors > 5%). The Two-Factor solution explained 33.24% of the total variance with a similar pattern in item loading. After comparing the Four-Factor through the Two-Factor models, it was the interpretation of the researcher that the analysis suggested the presence of one strong factor. The factor was labeled “general self-directed learning characteristics” which includes all 32 items used to assess characteristics associated with self-directed learning readiness. Table 15 shows the eigenvalues, factor loadings and variance explained for the 32 items on the Four-Factor rotated solution for the Self-Directed Learning Readiness part of the survey which illustrates the loading on one factor (Factor One). Table 15 Factor Loading, Eigenvalues, and Variance for Items Representing Self-Directed Learning Readiness for a Rotated Four-Factor Solution Item Number Factor 1 Factor 2 Factor 3 Factor 4 General Self-Directed Learning Characteristics SDL1 .358 SDL2 .398 .377 .324 SDL3 .423 .361 SDL4 .413 .371 SDL5 .625 SDL6 .417 SDL7 .412 (Table Continued)
104 SDL8 .507 SDL9 .481 -.337 SDL10 .414 -.361 SDL11 .569 SDL12 .598 SDL13 .635 SDL14 .605 SDL15 .523 SDL16 .403 SDL17 .570 SDL18 .521 -.355 SDL19 .606 -.409 SDL20 .613 SDL21 .688 -.462 SDL22 .662 -.464 SDL23 .410 .370 SDL24 .507 .337 SDL25 .559 SDL26 .624 SDL27 .377 .364 SDL28 .636 SDL29 .677 SDL30 .599 SDL31 .561 -.349 SDL32 .573 Eigenvalues 9.3 1.45 1.22 1.028 Variance Explained 29.06% 4.52% 3.82% 3.21% Note: Cross-loadings less than .30 are not listed in this table. Figure 1: Self-Directed Learning Readiness Four-Factor Solution Scree Plot
105 The factor comprised all the 32 items which assess self-directed learning readiness characteristics. A calculation of Cronbach’s alpha measure of internal consistency for the 32-item Self-Directed Learning Readiness part of the survey returned a high reliability score (α = .923). The overall item mean score for the factor was 3.28 (SD = .31) with the item means ranging from 2.55 to 3.67. This factor’s overall rating fell in the “strongly agree” category on the interpretive scale. The item with the highest mean value in this factor was SDL 11 “I am responsible” (M = 3.63, SD = .48) which fell in the “strongly agree” category on the interpretive scale. The item with the lowest mean value was SDL 23 “I set specific times for my study” (M = 2.54, SD = .63) which fell in the “agree” category on the interpretive scale. Readiness to Respond to Triggers for Learning Respondents were presented with a list of circumstances likely to occur in an adult’s life which may trigger participation in learning activities and were directed to rate the extent to which they would seek and participate in learning activities if the listed events were to occur in their lives. Each item measures the likelihood a respondent would participate in a learning activity when faced by each listed circumstance on a five-point Likert-type scale: 1= very unlikely, 2= unlikely, 3= likely, 4= very likely and 5= not applicable. However, there were some challenges in interpreting the “not applicable” score/selection. Coding the “not applicable” choice as a five would erroneously rank the likelihood that a respondent would participate in a learning activity as being higher than “very likely” on a circumstance which may not be applicable to the respondent. Also, the meaning of “not applicable” in any given circumstance is open to many interpretations. For instance, a selection of “not applicable” may mean that the circumstance presented is itself not applicable to that individual or it may also mean that participation in learning in the event that such a circumstance occurs is not applicable. For majority of the items, less than 10 percent selected the “not applicable” response. In a total of
106 nineteen of the twenty-eight items in this section, only five percent or fewer respondents selected the “not applicable” response. The researcher thus decided to treat all “not applicable” responses as missing data. The final response scale used in the analysis was therefore a four-point Likert-type scale: 1= very unlikely, 2= unlikely, 3= likely and 4= very likely. The following scale was created by the researcher to aid in the interpretation of the responses: 1 – 1.75= very unlikely, 1.76 – 2.50= unlikely, 2.51 – 3.25= likely and 3.26 – 4.00= very likely. First, the means and standard deviations of the responses to each item in the Readiness to Respond to Triggers for Learning Scale (RRT) were calculated. Table 16 below illustrates the mean scores and standard deviation for each item in the part of the scale representing the Readiness to Respond to Triggers for Learning (RRT) items. Table 16 Description of the Likelihood that Adult Volunteers Affiliated with the 4-H Youth Development Program in the Southern Region of the United States Would Seek and Participate in Learning when Faced with Triggers for Learning Readiness to Respond to Triggers for Learning Items Ma SD Categoryb RRT2. Major changes at work e.g. new equipment, new regulations 3.59 .713 VL RRT3. Getting a new major responsibility at work 3.56 .697 VL RRT5. Dealing with a major conflict with a close family member 3.54 .614 VL RRT13. Helping teenagers (children or siblings) become responsible adults 3.62 .642 VL RRT12. Need to help children/siblings go through School 3.48 .759 VL RRT20. Need to maintain good health 3.41 .672 VL RRT4. Seeing work colleagues get ahead in their Careers 3.40 .815 VL RRT14. A high price expenditure decision e.g. buying a house, car, or equipment 3.31 .912 VL RRT28. Changes in information technology e.g. computer programs 3.27 .738 VL RRT17. Rising cost of living 3.24 .736 L RRT1. Moving into a new job 3.23 .987 L RRT27. Loss of spouse or close family members 3.20 .777 L RRT6. Getting a promotion at work 3.15 .688 L (Table Continued)
107 RRT19. Injury or illness of a family member 3.07 .870 L RRT11. A close family member dealing with a crisis e.g. substance abuse 3.05 .964 L RRT18. Loss of personal health through injury or illness 3.02 .923 L RRT15. Reduction in family income 3.01 .940 L RRT23. Retirement 3.01 1.011 L RRT10. Need to improve relationships with close family members 3.00 .830 L RRT9. Getting a new baby through childbirth or adoption 2.99 .885 L RRT26. Changes in hobbies 2.85 .930 L RRT16. Increase in family income 2.82 .853 L RRT25. Changes in communication technology 2.77 .904 L RRT21. Moving to a new location e.g. neighborhood or city 2.67 1.062 L RRT22. Acquiring a new house or apartment 2.62 1.083 L RRT8. Entering a new marriage 2.52 1.160 L RRT24. Getting a divorce 2.34 1.117 U RRT7. Dealing with a specific immediate task at work 2.27 1.059 U Note: N= 277. Missing values replaced with variable mean a Response scale: 1 = very unlikely (VU), 2 = unlikely (U), 3 = likely (L) , and 4 = very likely (VL) b Interpretive scale: 1-1.75 = VU, 1.76–2.50 = U, 2.51–3.25 = L, and 3.26–4.00 = VL From the table above, the item that respondents expressed the highest likelihood of seeking and participating in learning activities was “Helping teenagers (children or sibling) become responsible adults” with a mean 3.62 (SD= 0.64). The item that received the second highest expression of likelihood that respondents would seek and participate in learning activities was “Major changes at work e.g. new equipment, new regulations” with a mean of 3.59 (SD= 0.70). Using the interpretive scale, both were in the “very likely” range. The item that respondents ranked as least likely to lead to participation in learning activities was “Dealing with a specific immediate task at work” with a mean of 2.27 (SD= 1.06). The item with the second lowest expression of likelihood was “Getting a divorce” with a mean of 2.34 (SD= 1.12). The response to both items fell within the “unlikely” range. Overall, the response to most items (17 items) fell within the “likely” range on the interpretive scale.
108 Several tests were undertaken to examine whether the data was suitable for factor analysis. A visual inspection of the correlation matrix showed that a substantial number of correlations were greater than 0.30. The Bartlett’s Test of Sphericity was found to be acceptable (3329.92; df= 378; p < .001). Finally, Kaiser-Meyer-Olkin (KMO) Measure of Sampling Adequacy test returned an acceptable score of 0.917. The data was thus deemed factorable. Common Factor Analysis with Principal Axis Factoring extraction was undertaken on the data. Promax (Oblique) rotation with Kaiser Normalization was undertaken to obtain a simpler, more interpretable factor structure. In determining the number of underlying factors to be extracted the researcher considered the Kaiser Criteria (factors with an eigenvalue greater than 1), percentage of variance criterion (percentage of total variance extracted by successive factors > 5%), and the Cattell Scree plot examination. Factor loadings greater than +/- 0.30 met the minimum criteria to be considered for interpretation. In addition to using the above guidelines, the researcher considered the simplicity, practicality and interpretability of the factors in determining the final factors to extract. After an initial exploratory factor analysis, a look at the scree plot and initial eigenvalues led to the consideration of a Three-Factor and Four-Factor solution. The Four-Factor solution explained 53.93% of the total variance. After an examination of both the factor pattern matrix and the factor structure matrix, fifteen items were determined to load on Factor One with values ranging from .337 to .880. Factor Two loaded with 5 items with numerical loading values ranging from .386 to .788. Factor Three loaded with 4 items with values ranging from .649 to .773. The Fourth Factor loaded 2 items with numerical loading values of .820 and .834. One item, “Getting a new baby through childbirth or adoption”, appeared to cross-load on Factor One (.357) and Factor Two (.386). All the four factors met the Kaiser Criteria (factors with an eigenvalue greater than 1). Three factors satisfied the percentage of variance criterion
109 (percentage of total variance extracted by successive factors > 5%). The Fourth Factor failed to meet the percentage of variance criterion (3.8% of total variance) and only 2 items loaded on it. The factors appeared impractical and were not amenable to easy interpretation. The Four Factor solution was therefore rejected and a Three Factor Solution examined. The Three-Factor solution yielded a model that explained 49.79% of the total variance. After an examination of both the factor pattern matrix and the factor structure matrix, 14 items appeared to load on Factor One with numerical loading values ranging from .332 to .874. Eight items loaded on Factor Two with values ranging from .359 to .739. Factor Three loaded 4 items with numerical loading values ranging from .671 to .775. There were two items which cross-loaded on two factors. The item “Getting a new baby through childbirth or adoption” did cross-load on Factor One (.368) and Factor Two (.466). The item was determined by the researcher as conceptually belonging to Factor One. The second item to cross-load was “Need to improve relationships with close family members” on Factor One (.493) and Factor Two (.359). The item was determined by the researcher as conceptually belonging to Factor Two. All the three factors met the Kaiser Criteria (factors with an eigenvalue greater than 1) and the percentage of variance criterion (percentage of total variance extracted by successive factors > 5%). This model also better met the criteria for being a simple interpretable structure. The model was thus determined to best represent the responses to the Readiness to Respond to Triggers for Learning Scale. Factor One which was labeled “primary changes” had 15 items. Factor Two was composed of 9 items and was labeled “secondary changes”. Factor Three was labeled “work changes” and had 4 items. Table 17 shows the eigenvalues, factor loadings and variance explained for the 28 items on the Three-Factor rotated solution for the Readiness to Respond to Triggers for Learning part of the survey. Below the table is a scree plot figure used to arrive at a Three-Factor rotated solution.
110 Table 17 Factor Loading, Eigenvalues, and Variance for Items Representing Readiness to Respond to Triggers for Learning for a Rotated Three-Factor Solution Item Number Factor 1 Factor 2 Factor 3 Primary Changes Secondary Changes Work Changes RRT 24 .874 RRT 8 .870 RRT7 .861 RRT21 .815 RRT18 .770 RRT22 .731 RRT11 .661 RRT15 .656 RRT19 .650 RRT25 .612 RRT26 .601 RRT23 .576 RRT14 .468 RRT9 .368 .466 RRT16 .332 RRT17 .739 RRT20 .671 RRT13 .649 RRT12 .536 RRT5 .526 RRT27 .450 RRT28 .428 RRT6 .368 RRT10 .493 .359 RRT3 .775 RRT2 .747 RRT4 .693 RRT1 .671 Eigenvalues 9.99 2.57 1.38 Variance Explained 35.69% 9.18% 4.93% Note: Cross-loadings less than .30 are not listed in this table.
111 Figure 2: Readiness to Respond to Triggers for Learning Three-Factor Solution Scree Plot Factor One labeled “primary changes” had 15 items assessing the likelihood that volunteers would participate in learning to address what can be classified as major changes in life circumstances. These are mostly one-time challenges or changes in life circumstances which cannot be classified as everyday occurrences which nonetheless affect one’s life flow and have to be attended to. A calculation of Cronbach’s alpha measure of internal consistency for the 15 items comprising Factor One returned a high reliability score (α = .946). The overall item mean score for the factor was 2.81 (SD = 1.01) with the item means ranging from 2.32 to 3.28. This factor’s overall rating fell in the “likely” category on the interpretive scale. The item with the highest mean value in this factor was RRT 14 “A high price expenditure decision e.g. buying a house, car, equipment” (M = 3.27, SD = .91). The item with the lowest mean value was RRT 7 “Dealing with a specific immediate task at work” (M = 2.32, SD = 1.67). The second factor labeled “secondary changes” had 9 items which assessed the likelihood that volunteers would participate in learning to address what can be classified as everyday life challenges or life maintenance challenges. Cronbach alpha measure of internal consistency was calculated for the 9 items comprising Factor Two which returned a high reliability score (α =
112 .824). The overall item mean score for the factor was 3.31 (SD = .55) with the item means ranging from 2.98 to 3.61. This factor’s overall rating fell in the “very likely” category on the interpretive scale. The item with the highest mean value in this factor was RRT 20 “Need to maintain good health” (M = 3.39, SD = .67). The item with the lowest mean value was RRT 10 “Need to improve relations with close family members” (M = 2.98, SD = .84). The third factor labeled “work changes” had 4 items which addressed the likelihood adults undertook learning activities to address work-related changes. A calculation of Cronbach’s alpha measure of internal consistency for the 4 items comprising Factor Three returned a high reliability score (α = .872). The overall item mean score for the factor was 3.47 (SD = .70) with the item means ranging from 3.28 to 3.60. This factor’s overall rating fell in the “very likely” category on the interpretive scale. The item with the highest mean value in this factor was RRT 2 “Major changes at work e.g. new equipment, new regulations” (M = 3.60, SD = .74). The item with the lowest mean value was RRT 1 “Moving into a new job” (M = 3.28, SD = .99). Readiness to Overcome Deterrents to Participation in Learning The items in this section of the survey assessed the readiness of respondents to overcome some deterrents to participation in learning. Respondents were directed to rate the extent to which each item measured a characteristic of themselves on a four-point Likert-type scale: 1= strongly disagree, 2= disagree, 3= agree, and 4= strongly agree. The following scale was created by the researcher to aid in the interpretation of the responses: 1 – 1.75= strongly disagree, 1.76 – 2.50= disagree, 2.51 – 3.25= agree, and 3.26 – 4.00= strongly agree. The mean and standard deviation of the responses to each item in the Readiness to Overcome Deterrents to Participation (ROD) part of the survey was calculated. The item that received the highest level of agreement from respondents was “I can learn regardless of my age” with a mean 3.54 (SD= 0.51). The item that received the second highest level of agreement from
113 respondents was “Age cannot keep me from learning what I need to learn” with a mean of 3.53 (SD= 0.51). Using the interpretive scale, both were in the “strongly agree” range. The item with the lowest level of agreement was “Costs cannot keep me from learning what I need to learn” with a mean of 2.73 (SD= 0.76). The item with the second lowest level of agreement was “I always find ways to cover the costs for the learning I need” with a mean of 2.94 (SD= 0.63). The response to both items fell within the “agree” range. Overall, almost half of the responses (8 items) fell within the “agree” range on the interpretive scale, while the rest (7 items) fell within the strongly agree range. Table 18 below illustrates the mean score and standard deviation for each item representing respondent’s level of agreement with their readiness to overcome deterrents (ROD) to participation in learning. Table 18 Description of the Level of Agreement of Adult Volunteers Affiliated with the 4-H Youth Development Program in the Southern Region of the United States with Statements Reflecting the Readiness to Overcome Deterrents to Participation in Learning Readiness to Overcome Deterrents Items Ma SD Categoryb ROD10. I can learn regardless of my age 3.54 .512 SA ROD11. Age cannot keep me from learning what I need to learn 3.53 .511 SA ROD14. I can use technology to access a variety of learning activities 3.41 .559 SA ROD13. I am confident in my ability to search for information online 3.40 .581 SA ROD9. I am confident of my learning ability 3.39 .542 SA ROD15. I am confident in my ability to use technology in learning 3.34 .615 SA ROD12. I do what it takes to get ready to learn what I need to learn 3.29 .547 SA ROD2. I am capable of finding good quality learning Activities 3.18 .519 A ROD3. I search until I find learning activities that fit my schedule 3.16 .544 A ROD1. I search until I find learning activities that 3.12 .539 A meet my learning needs ROD6. I always find a cost effective way to learn what I need to learn 3.03 .541 A (Table continued)
114 ROD4. I always make time to learn when I need to 2.99 .667 A ROD5. I am able to balance time between family and 2.99 .590 A learning activities ROD7. I always find ways to cover the costs for the 2.94 .629 A learning I need ROD8. Costs cannot keep me from learning what I 2.73 .764 A need to learn Note: N= 277. Missing values replaced with variable mean a Response scale: 1 = strongly disagree (SD), 2 = disagree (D), 3 = agree (A), and 4 = strongly agree (SA) b Interpretive scale: 1 – 1.75= SD, 1.76 – 2.5= D, 2.51 – 3.25= A, and 3.26 – 4.00= SA Several tests determined that the data was factorable. A visual inspection of the correlation matrix showed that a substantial number of correlations were greater than 0.30. The Bartlett’s Test of Sphericity was found to be acceptable (2431.66; df= 105; p < .001). Finally, Kaiser-Meyer-Olkin (KMO) Measure of Sampling Adequacy test returned an acceptable score of 0.855. Common Factor Analysis with Principal Axis Factoring extraction was undertaken on the data. Promax (Oblique) rotation with Kaiser Normalization was undertaken to obtain a simpler, more interpretable factor structure. In determining the number of underlying factors to be extracted the researcher considered the Kaiser Criteria (factors with an eigenvalue greater than 1), percentage of variance criterion (percentage of total variance extracted by successive factors > 5%), and the Cattell Scree plot examination. Factor loadings greater than +/- 0.30 met the minimum criteria to be considered for interpretation. In addition to using the above guidelines in determining the factors to extract, the simplicity, practicality and interpretability of the factors affected the final factor solution that this researcher arrived at. An exploratory factor analysis procedure performed on this data revealed a Four-Factor solution which explained 63.27% of the total variance. An examination of the scree plot indicated a flattening after the Fourth Factor. All four factors met the percentage of variance criterion (percentage of total variance extracted by successive factors > 5%). At least the first three factors met the Kaiser Criteria (factors with an eigenvalue greater than 1). The fourth factor
115 which had an eigenvalue of .77 (less than 1.0) was included in the factor solution since it explained more than 5% (percentage of variance criterion), and the scree plot clearly showed four factors. An examination of the factor matrix revealed some strong cross-loadings which presented a challenge in clearly delineating the items belonging to each factor and interpreting the factors. The factor pattern matrix was used to help better delineate, label and interpret the factors. When a factor pattern matrix is examined identified themes more obvious (Pett, Lackey, & Sullivan, 2003). For the Four-Factor solution, a total of 5 items loaded on Factor One with numerical loading values ranging from .901 to .324. Four items loaded on Factor Two with numerical loading values ranging from .966 to .382. Factor Three had a total of 3 items load on it with numerical loading values ranging from .977 to .630. Three items loaded on Factor Four with numerical loading values ranging from .915 to .830. There was one item, “I am able to balance time between family and learning activities”, which cross-loaded on Factor One and Factor Three. The item was determined by the researcher as conceptually belonging to Factor One more than Factor Three. The Four Factor model was retained owing to its simple structure and interpretability. The four factors were labeled “programmatic issues”, “dispositional issues”, “cost issues”, and “learning technology issues”. Table 19 shows the eigenvalues, factor loadings and variance explained for the 16 items on the Four-Factor rotated solution for the Readiness to Overcome Deterrents to Participation in Learning part of the survey. Table 19 Factor Loading, Eigenvalues, and Variance for Items Representing Readiness to Overcome Deterrents to Participation in Learning for a Rotated Four-Factor Solution Item Number Factor 1 Factor 2 Factor 3 Factor 4 Programmatic Dispositional Cost Learning Technology ROD1 .901 ROD2 .757 ROD3 .662 (Table Continued)
116 ROD4 .462 ROD5 .324 .390 ROD11 .966 ROD10 .993 ROD9 .563 ROD12 .382 ROD7 .977 ROD8 .768 ROD6 .630 ROD14 .915 ROD15 .896 ROD13 .830 Eigenvalues 6.02 1.66 1.04 .77 Variance Explained 40.14% 11.06% 6.93% 5.13% Note. Cross-loadings less than .30 are not listed in this table. Figure 3: Readiness to Overcome Deterrents to Participation in Learning Four-Factor Solution Scree Plot Factor One which had a total of 5 items was labeled “programmatic issues” which addressed the ability to overcome deterrents related to finding learning activities that met one’s learning needs and convenience. Cronbach alpha measure of internal consistency was calculated for the 5 items comprising Factor One which returned a high reliability score (α = .808). The overall item mean score for the factor was 3.084 (SD = .33) with the item means ranging from 2.981 to 3.175. This factor’s overall rating fell in the “agree” category on the interpretive scale.
117 The item with the highest mean value in this factor was ROD 2 “I am capable of finding good quality learning activities” (M = 3.18, SD = .52). The item with the lowest mean value was ROD 4 “I always make time to learn when I need to” (M = 2.98, SD = .67). Factor Two labeled “dispositional issues” loaded 4 items addressing the ability to overcome deterrents related to self-perceptions and attitudes of learners regarding their ability to learn. A calculation of Cronbach’s alpha measure of internal consistency for the 4 items comprising Factor Two returned a high reliability score (α = .858). The overall item mean score for the factor was 3.439 (SD = .28) with the item means ranging from 3.305 to 3.538. This factor’s overall rating fell in the “strongly agree” category on the interpretive scale. The item with the highest mean value in this factor was ROD 10 “I can learn regardless of my age” (M = 3.54, SD = .51). The item with the lowest mean value was ROD 12 “I do what it takes to get ready to learn what I need to learn” (M = 3.30, SD = .54). Factor Three labeled “cost issues” loaded 3 items addressed the ability to overcome cost deterrents to participation in learning. Cronbach alpha measure of internal consistency was calculated for the 3 items comprising Factor Three which returned a high reliability score (α = .809). The overall item mean score for the factor was 2.900 (SD = .43) with the item means ranging from 2.723 to 3.033. This factor’s overall rating fell in the “agree” category on the interpretive scale. The items had the following mean scores: ROD 6 “I always find a cost effective way to learn what I need to learn” had the highest item mean score of 3.03 (SD = .54); ROD 7 “I always find ways to cover the costs for the learning I need to learn” with a mean of2.94 (SD = .63); and ROD 8 “Costs cannot keep me from learning what I need to learn” had the lowest item mean (M = 2.72, SD = .77). The fourth factor labeled “learning technology issues” loaded 3 items which addressed the volunteer’s ability to overcome deterrents related to using technology for learning. Cronbach
118 alpha measure of internal consistency was calculated for the 3 items comprising Factor Four which returned a high reliability score (α = .936). The overall item mean score for the factor was 3.38 (SD = .35) with the item means ranging from 3.34 to 3.40. This factor’s overall rating fell in the “strongly agree” category on the interpretive scale. The items had the following mean scores: ROD 14 “I can use technology to access a variety of learning activities” had the highest item mean score of 3.40 (SD = .56); ROD 13 “I am confident in my ability to search for information online” with a mean of 3.39 (SD = .59); and ROD 15 “I am confident in my ability to use technology in learning” had the lowest item mean (M = 3.34, SD = .62). Overall Readiness for Lifelong Learning Score The overall readiness for lifelong learning score was obtained by summing the sub-scale scores from the three sections of the Readiness for Lifelong Learning Questionnaire. The item mean for the overall score (N = 277) was 3.198 and the standard deviation .312. An interpretive score was developed by the researcher to help interprete the overall readiness for lifelong learning score. Based on that interpretive scale, the mean fell within the “high readiness” category. The item mean for the overall score ranged from 2.51 (high readiness) to 3.95 (very high readiness). The interpretive scale was:  1.00 – 1.75 = very low readiness  1.76 – 2.5 = low readiness  2.51 – 3.25 = high readiness  3.26 – 4.00 = very high readiness A very high readiness for lifelong learning indicates that an adult is very likely to engage in lifelong learning or be a lifelong learner. The person identifies him/herself as being more likely to respond to circumstances known to trigger adult learning with participation in learning,
119 has self-directed learning characteristics, and is likely to overcome known deterrents to adult participation in learning. These when combined indicate a readiness to learn throughout life. Objective Three Objective three was to determine if differences exist in the readiness for lifelong learning as measured by the Readiness for Lifelong Learning Scale on selected demographic characteristics which include: a) Gender b) Ethnicity c) Highest educational level completed d) Yearly net income e) Marital Status f) Whether or not the volunteer has children living at home g) Employment status h) Current occupational Category i) Whether or not volunteer’s current employment requires continuous certification Gender A comparison of the overall readiness for lifelong learning score between males and females was undertaken through calculation of one way analysis of variance (ANOVA). The mean item score for males was slightly lower than that for females (Table 20). Table 20 Group Sizes, Overall Readiness for Lifelong Learning Item Mean Scores, and Standard Deviation by Gender for Volunteers Affiliated with a 4-H Youth Development Program in the Southern Region of the United States Item Mean Gender n M a SD Male 44 3.136 0.263 (Table Continued)
120 Female 230 3.211 0.320 Totalb 274 3.199 0.313 Note: Three respondents failed to respond to the gender item or provide data for calculation of the overall readiness for lifelong learning score on the questionnaire a Interpretive scale: 1.00 – 1.75 = very low readiness; 1.76 – 2.5 = low readiness; 2.51 – 3.25 = high readiness; and 3.26 – 4.00 = very high readiness b Reported as overall item mean and standard deviation Results from Levenes Test of Homogeneity of Variance revealed the presence of equal variance between the different gender groups (F1, 272 = 2.532, p = .113). The differences in overall readiness for lifelong learning score between the gender groups were not statistically significant (F1, 272 = 2.117, p = .147). Table 21 illustrates the ANOVA results for differences in overall readiness for lifelong learning by gender. Table 21 One Way Analysis of Variance Illustrating Differences in Overall Readiness for Lifelong Learning by Gender for Volunteers Affiliated with a 4-H Youth Development Program in the Southern Region of the United States df SS MS Fa Pb Between Groups 1 .206 .206 2.117 .147 Within Groups 272 26.491 .097 Total 273 26.698 a One Way Analysis of Variance b .05 Alpha Level for the Two-Tailed Test of Significance Ethnicity Differences in overall lifelong learning readiness scores were also examined by ethnicity. The sample sizes, overall readiness for lifelong learning score item means and standard deviations reported by ethnicity are illustrated in Table 22. Table 22 Group Sizes, Overall Readiness for Lifelong Learning Item Mean Scores, and Standard Deviation by Ethnicity for Volunteers Affiliated with a 4-H Youth Development Program in the Southern Region of the United States Item Mean Ethnicity n M a SD African American 28 3.260 .312 Asian 1 3.351 0 (Table Continued)
121 Caucasian 238 3.192 .316 Hispanic 1 3.541 0 Native American 1 3.280 0 Other 4 3.052 .193 Totalb 273 3.199 .313 Note: Four respondents failed to respond to the ethnicity item or provide data for calculation of the overall readiness for lifelong learning score on the questionnaire a Interpretive scale: 1.00 – 1.75 = very low readiness; 1.76 – 2.5 = low readiness; 2.51 – 3.25 = high readiness; and 3.26 – 4.00 = very high readiness b Reported as overall item mean and standard deviation The findings illustrated in Table 23 indicate that there were no significant differences in the overall readiness for lifelong learning score within the reported ethnic groups (F5, 267 = .710, p = .616). The Levenes Test of Homogeneity of Variance revealed the presence of equal variance between the different ethnic groups (F2, 267 = .705, p = .495). Table 23 One Way Analysis of Variance Illustrating Differences in Overall Readiness for Lifelong Learning by Ethnicity for Volunteers Affiliated with a 4-H Youth Development Program in the Southern Region of the United States df SS MS Fa Pb Between Groups 5 .350 .070 .710 .616 Within Groups 267 26.347 .099 Total 272 26.698 a One Way Analysis of Variance b .05 Alpha Level for the Two-Tailed Test of Significance Highest Level of Education Completed A comparison of the overall readiness for lifelong learning score by the respondents highest level of education completed was undertaken through calculation of one way analysis of variance (ANOVA). The Levenes Test of Homogeneity of Variance revealed the presence of equal variance between the different groups based on highest level of education completed (F8, 263 = 1.057, p = .394). The overall mean score and standard deviation for the various groups was calculated. The mean item score was highest for the “doctoral degree” category, the score for which fell in the “very high readiness” category in the interpretive scale (Table 24).
122 Table 24 Group Sizes, Overall Readiness for Lifelong Learning Item Mean Scores, and Standard Deviation by Highest Level of Education Completed for Volunteers Affiliated with a 4-H Youth Development Program in the Southern Region of the United States (Table Continued) Item Mean Highest Level of Education Completed n M a SD High School Diploma 14 3.172 .369 Some Vocational/Technical School 5 3.069 .311 Vocational/Technical School Degree 8 2.957 .218 Some College 26 3.166 .223 Associate Degree 13 3.177 .272 Bachelors Degree (BA/BS) 117 3.213 .335 Masters Degree (MA/MS/MBA) 82 3.219 .304 Professional Degree (JD/MD) 4 3.346 .281 Doctoral Degree (Ph.d/Ed.d 3 3.448 .250 Totalb 272 3.200 .312 Note: Five respondents failed to respond to the highest level of education completed item or provide data for calculation of the overall readiness for lifelong learning score a Interpretive scale: 1.00 – 1.75 = very low readiness; 1.76 – 2.5 = low readiness; 2.51 – 3.25 = high readiness; and 3.26 – 4.00 = very high readiness b Reported as overall item mean and standard deviation Although there were some differences in the overall readiness for lifelong learning score based on the highest level of education completed, none of the differences were statistically significant (Table 25). Table 25 One Way Analysis of Variance Illustrating Differences in Overall Readiness for Lifelong Learning by the Highest Level of Education Completed for Volunteers Affiliated with a 4-H Youth Development Program in the Southern Region of the United States df SS MS Fa Pb Between Groups 8 .925 .116 1.193 .303 Within Groups 263 25.483 .097 Total 271 26.408 a One Way Analysis of Variance b .05 Alpha Level for the Two-Tailed Test of Significance Yearly Net Income Differences in overall lifelong learning readiness scores were also examined by the reported yearly net income. The yearly net income category with the highest mean item score
123 (M = 3.26) was the “$50,000 – $75,000” category, which is categorized as “very high readiness” in the interpretive scale. The sample sizes, overall readiness for lifelong learning score item means and standard deviations reported by yearly net income are illustrated in Table 26. Table 26 Group Sizes, Overall Readiness for Lifelong Learning Item Mean Scores, and Standard Deviation by Yearly Net Income for Volunteers Affiliated with a 4-H Youth Development Program in the Southern Region of the United States Item Mean Yearly Net Income n M a SD Less than 25,000 28 3.104 .307 25,001-50,000 107 3.227 .333 50,001-75,000 70 3.264 .333 75,001-100,000 35 3.216 .219 Greater than 100,000 19 3.019 .216 Totalb 259 3.207 .315 Note: Eighteen respondents failed to respond to the yearly net income item or provide data for calculation of the overall readiness for lifelong learning score on the questionnaire a Interpretive scale: 1.00 – 1.75 = very low readiness; 1.76 – 2.5 = low readiness; 2.51 – 3.25 = high readiness; and 3.26 – 4.00 = very high readiness b Reported as overall item mean and standard deviation The Levenes Test of Homogeneity of Variance revealed a violation of the assumption of equal variances among the groups (F4, 254 = 3.858, p = .005). A calculation of the Welch Statistic which accounts for the lack of homogeneity of variance revealed statistically significant differences in the overall readiness for lifelong learning score based on yearly net income (4.742; 4, 78.297; p = .002). The Tukey’s post hoc analysis used to locate the significant differences between means revealed significant differences in the overall readiness for lifelong learning score between those reporting more than $100,000 yearly net income and those reporting a yearly net income of between $50,001 and $75,000 (mean difference = .25). Marital Status A comparison of the overall readiness for lifelong learning score by the respondents reported marital status was also undertaken. The mean item score was highest for the “divorced”
124 category, the score for which fell in the “very high readiness” category in the interpretive scale (Table 27). Table 27 Group Sizes, Overall Readiness for Lifelong Learning Item Mean Scores, and Standard Deviation by Marital Status for Volunteers Affiliated with a 4-H Youth Development Program in the Southern Region of the United States Item Mean Marital Status n M a SD Single Never Married 26 3.074 .345 Married 214 3.194 .308 Separated 2 3.517 .241 Divorced 20 3.346 .295 Widowed 8 3.270 .280 Totalb 270 3.198 .314 Note: Seven respondents failed to respond to the marital status item or provide data for calculation of the overall readiness for lifelong learning score on the questionnaire a Interpretive scale: 1.00 – 1.75 = very low readiness; 1.76 – 2.5 = low readiness; 2.51 – 3.25 = high readiness; and 3.26 – 4.00 = very high readiness b Reported as overall item mean and standard deviation The findings illustrated in Table 28 indicate that there were significant differences in the overall readiness for lifelong learning score within the groups based on marital status (F4, 265 = 2.819, p = .026). The Levenes Test of Homogeneity of Variance revealed the presence of equal variance between the different groups based on marital status (F4, 265 = .424, p = .792). Table 28 One Way Analysis of Variance Illustrating Differences in Overall Readiness for Lifelong Learning by Marital Status for Volunteers Affiliated with a 4-H Youth Development Program in the Southern Region of the United States df SS MS Fa Pb Between Groups 4 1.084 .271 2.819 .026 Within Groups 265 25.487 .096 Total 269 26.571 a One Way Analysis of Variance b .05 Alpha Level for the Two-Tailed Test of Significance The Tukey’s post hoc analysis used to pin-point the significant differences between means revealed significant differences in the overall readiness for lifelong learning score
125 between those who reported being “single/never married” and those who reported being “divorced” (mean difference = -.27). Presence of Children at Home Respondents were also asked if they had any children at home. An examination of the differences in the overall readiness for lifelong learning score based on the respondents report on the presence or absence of children at home was undertaken. Results from Levenes Test of Homogeneity of Variance revealed the presence of equal variance between the two groups (F1, 269 = .092, p = .762). Those with children at home had a slightly higher readiness for lifelong learning item mean score (See Table 29). Table 29 Group Sizes, Overall Readiness for Lifelong Learning Item Mean Scores, and Standard Deviation Based on Presence of Children at Home for Volunteers Affiliated with a 4-H Youth Development Program in the Southern Region of the United States Item Mean Presence of Children at Home n M a SD Presence of Children at Home (Yes) 184 3.208 0.311 Absence of Children at Home (No) 87 3.169 0.312 Totalb 271 3.196 0.311 Note: Six respondents failed to respond to the presence of children at home item or provide data for calculation of the overall readiness for lifelong learning score on the questionnaire a Interpretive scale: 1.00 – 1.75 = very low readiness; 1.76 – 2.5 = low readiness; 2.51 – 3.25 = high readiness; and 3.26 – 4.00 = very high readiness b Reported as overall item mean and standard deviation Below, Table 30 shows that the differences in overall readiness for lifelong learning score between those with children at home and those without were not statistically significant (F1, 269 = .886, p = .347). Table 30 One Way Analysis of Variance Illustrating Differences in Overall Readiness for Lifelong Learning Based on Presence of Children at Home for Volunteers Affiliated with a 4-H Youth Development Program in the Southern Region of the United States df SS MS Fa Pb Between Groups 1 .086 .086 .886 .347 (Table Continued)
126 Within Groups 269 26.058 .097 Total 270 26.144 a One Way Analysis of Variance b .05 Alpha Level for the Two-Tailed Test of Significance Employment Status Differences in overall lifelong learning readiness scores were also examined by respondents reported current employment status. The group reporting the highest overall readiness for lifelong learning mean item score (M = 3.326) which was categorized as “very high readiness” on the interpretive scale was the group “employed on a contract basis”. The sample sizes, overall readiness for lifelong learning score item means and standard deviations reported by current employment status are illustrated in Table 31. Table 31 Group Sizes, Overall Readiness for Lifelong Learning Item Mean Scores, and Standard Deviation by Employment Status for Volunteers Affiliated with a 4-H Youth Development Program in the Southern Region of the United States Item Mean Employment Status n M a SD Unemployed 10 3.189 .272 Employed Full Time 226 3.216 .317 Employed on a Contract Basis 9 3.236 .346 Employed Part Time 17 3.003 .248 Retired 12 3.161 .264 Totalb 274 3.199 .313 Note: Three respondents failed to respond to the current employment status item or provide data for calculation of the overall readiness for lifelong learning score on the questionnaire a Interpretive scale: 1.00 – 1.75 = very low readiness; 1.76 – 2.5 = low readiness; 2.51 – 3.25 = high readiness; and 3.26 – 4.00 = very high readiness b Reported as overall item mean and standard deviation The findings illustrated in Table 32 indicate that there were no significant differences in the overall readiness for lifelong learning score within the groups based on current employment status (F4, 269 = 1.918, p = .108). The Levenes Test of Homogeneity of Variance revealed the
127 presence of equal variance between the different groups categorized on the reported employment status (F4, 269 = .560, p = .692). Table 32 One Way Analysis of Variance Illustrating Differences in Overall Readiness for Lifelong Learning Based on the Current Employment Status of Volunteers Affiliated with a 4-H Youth Development Program in the Southern Region of the United States df SS MS Fa Pb Between Groups 4 .740 .185 1.918 .108 Within Groups 269 25.957 .096 Total 273 26.698 a One Way Analysis of Variance b .05 Alpha Level for the Two-Tailed Test of Significance Current Occupational Category Differences in overall lifelong learning readiness scores were also examined by the respondent’s current occupational category. The sample sizes, overall readiness for lifelong learning score item means and standard deviations reported by current occupational category are illustrated in Table 33. Table 33 Group Sizes, Overall Readiness for Lifelong Learning Item Mean Scores, and Standard Deviation by Current Occupational Category for Volunteers Affiliated with a 4-H Youth Development Program in the Southern Region of the United States Item Mean Current Occupational Category n M a SD Professional/Managerial 217 3.213 .315 Sales/Service/Support 22 3.104 .342 Trade/Labor 5 3.142 .138 Total 244 3.202 .316 Note: Thirty three respondents failed to respond to the ethnicity item or provide data for calculation of the overall readiness for lifelong learning score on the questionnaire a Interpretive scale: 1.00 – 1.75 = very low readiness; 1.76 – 2.5 = low readiness; 2.51 – 3.25=high readiness; and 3.26 – 4.00 = very high readiness Results from Levenes Test of Homogeneity of Variance revealed the presence of equal variance between the three occupational categories (F2, 241 = 2.074, p = .128). The differences in overall readiness for lifelong learning score between the groups based on different occupational
128 categories were not statistically significant (F2, 241 = 1.288, p = .278). Table 34 illustrates the ANOVA results for differences in overall readiness for lifelong learning based respondents reported current occupational category. Table 34 One Way Analysis of Variance Illustrating Differences in Overall Readiness for Lifelong Learning Based on the Current Occupational Category of Volunteers Affiliated with a 4-H Youth Development Program in the Southern Region of the United States df SS MS Fa Pb Between Groups 2 .257 .128 1.288 .278 Within Groups 241 24.023 .100 Total 243 24.280 a One Way Analysis of Variance b .05 Alpha Level for the Two-Tailed Test of Significance Current Employment Requires Continuous Certification/Licensure Respondents were also asked to indicate whether their current employment required continuous certification or licensure. An examination of the differences in the overall readiness for lifelong learning score based on whether their current employment required continuous certification/licensure or not was undertaken. Those whose employment required certification had a slightly higher readiness for lifelong learning item mean score (Table 35). Table 35 Group Sizes, Overall Readiness for Lifelong Learning Item Mean Scores, and Standard Deviation Based on Whether Current Employment Reported by Volunteers Affiliated with a 4-H Youth Development Program in the Southern Region of the United States Required Continuous Certification/Licensure Current Employment Requires Item Mean Continuous Certification/Licensure n M a SD Requires Certification/Licensure (Yes) 175 3.218 0.321 Does not Require Certification/Licensure (No) 87 3.149 0.301 Totalb 262 3.195 0.315 Note: Fifteen respondents failed to respond to the item on whether their current employment required continuous certification/licensure or provide data for calculation of the overall readiness for lifelong learning score on the questionnaire. a Interpretive scale: 1.00 – 1.75 = very low readiness; 1.76 – 2.5 = low readiness; 2.51 – 3.25 = high readiness; and 3.26 – 4.00 = very high readiness b Reported as overall item mean and standard deviation
129 Results from Levenes Test of Homogeneity of Variance revealed the presence of equal variance between the two groups (F1, 260 = .024, p = .878). The differences in overall readiness for lifelong learning score based on whether current occupation requires continuous certification/licensure or not were not statistically significant (F1, 260 = 2.835, p = .093). Table 36 illustrates the ANOVA results for differences in overall readiness for lifelong learning based on whether respondent’s current employment required continuous certification/licensure or not. Table 36 One Way Analysis of Variance Illustrating Differences in Overall Readiness for Lifelong Learning Based on Whether Current Employment Reported by Volunteers Affiliated with a 4-H Youth Development Program in the Southern Region of the United States Required Continuous Certification/Licensure df SS MS Fa Pb Between Groups 1 .280 .280 2.835 .093 Within Groups 260 25.699 .099 Total 261 25.979 a One Way Analysis of Variance b .05 Alpha Level for the Two-Tailed Test of Significance Format in Which Respondents Prefer Learning Differences in overall lifelong learning readiness scores were also examined by the respondents reported preferred format for learning. The sample sizes, overall readiness for lifelong learning score item means and standard deviations reported by respondents preferred format for learning are illustrated in Table 37. Table 37 Group Sizes, Overall Readiness for Lifelong Learning Item Mean Scores, and Standard Deviation by Preferred Format for Learning for Volunteers Affiliated with a 4-H Youth (Table Continued) Development Program in the Southern Region of the United States Item Mean Preferred Format for Learning n M a SD Formal Classes 14 3.163 .316 Workshops 154 3.166 .295 (Table Continued)
130 Web-based/online training 69 3.307 .296 Mail correspondence 8 3.284 .375 Mentoring 14 3.111 .407 Totalb 259 3.204 .310 Note: Eighteen respondents failed to respond to the item on their preferred format for learning or provide data for calculation of the overall readiness for lifelong learning score on the questionnaire a Interpretive scale: 1.00 – 1.75 = very low readiness; 1.76 – 2.5 = low readiness; 2.51 – 3.25 = high readiness; and 3.26 – 4.00 = very high readiness b Reported as overall item mean and standard deviation Results from Levenes Test of Homogeneity of Variance revealed the presence of equal variance between the three occupational categories (F4, 254 = 1.548, p = .189). The differences in overall readiness for lifelong learning score between the groups based on their preferred format for learning were statistically significant (F4, 254 = 3.081, p = .017). Table 38 below illustrates the ANOVA results. Table 38 One Way Analysis of Variance Illustrating Differences in Overall Readiness for Lifelong Learning Based on the Reported Preferred Format for Learning for Volunteers Affiliated with a 4-H Youth Development Program in the Southern Region of the United States df SS MS Fa Pb Between Groups 4 1.150 .287 1.288 .017 Within Groups 254 23.703 .093 Total 258 24.853 a One Way Analysis of Variance b .05 Alpha Level for the Two-Tailed Test of Significance The Tukey’s post hoc analysis revealed significant differences in the overall readiness for lifelong learning score between those who reported preference for “Web-based/online training” learning format and those who reported preference for “Workshops” learning format (mean difference = .141). Objective Four Objective four was to determine if a model exists which would explain a significant portion of the variance of readiness for lifelong learning as measured by the overall readiness for
131 lifelong learning item mean score and the demographic characteristics of age, gender, ethnicity, highest educational level completed, yearly net income, marital status, length in current employment, and the format in which respondents prefer learning. Respondent’s scores from the three sections of the readiness for lifelong learning questionnaire were summed up to obtain the overall readiness for lifelong learning score. The overall item mean score for each respondent was thus calculated from the overall readiness for lifelong learning score and utilized as the dependent variable in the regression equation. The variables “age” and “length in current employment” were entered into the regression as interval variables. For the categorical independent variables dummy coding was undertaken for regression analysis. In some cases the levels of the independent categorical variables were combined to form new categories. The variable “highest education level completed” which originally had 10 levels was combined into three levels namely “some college”, “bachelors degree” and “graduate degree” which were then dummy coded. The variable “ethnicity” which originally had 6 levels was combined into two levels namely “Caucasian” and “non-Caucasian” which were then dummy-coded. The independent variables “yearly net income”, “marital status”, “gender”, and “format in which respondents prefer learning” were dummy coded including all their original categories. A graphic histogram illustration of the plotted standardized residuals for the dependent variable Overall Mean shows an approximation of a normal curve, and thus normality is assumed (See Figure 4 below).
132 Figure 4: Histogram Depicting Standardized Residuals for the Dependent Variable Overall Mean A bivariate Pearson product moment correlation was undertaken between the overall readiness for lifelong learning score (dependent variable) and the independent variables. Within each categorical variable, the level of the variable whose correlation with the dependent variable was least significant was dropped from further analysis. The dropped independent variable levels included: “high school diploma” in the “highest education level completed” variable (n = 272, r = -.021, p = .724); “yearly net income $75,001 – 100,000” (n = 259, r = .012, p = .850); “married” in the marital status variable (n = 270, r = -.028, p = .650); and “mail correspondence” in the preferred format for learning (n = 259, r = .046, p = .460). The remaining independent variables were entered stepwise into the regression analysis with the overall readiness for lifelong learning item mean score entered as the dependent variable. Several diagnostic checks for collinearity suggested by Hair, Anderson, Tatham, and Black (1998) were undertaken. An examination of the correlation matrix for independent
133 variables did not reveal any high correlations. A look at the variance inflation factor (VIF) and the tolerance values did not indicate presence of a collinearity problem. Four variables were retained in the equation and determined to explain approximately 9% (R2 = .093) of the variance in the overall readiness for lifelong learning score. The regression equation with the four independent variables was found to be significant in predicting the overall readiness for lifelong learning score (F4, 272 = 6.937, p = <.001). All the four variables significantly contributed to the model: “web-based/online training” learning format (t = 3.178, p = .002); “more than 100,001” yearly net income (t = -2.541, p = .012), “single/never married” marital status (t = -2.286, p = .023), and “divorced” marital status (t = 2.007, p = .046). Table 39 illustrates the ANOVA and model summary results for the regression equation employing four independent variables in predicting the overall readiness for lifelong learning score and the model summary. Table 39 Significance of the Regression Equation and Model Summary Employing Four Independent Variables in Predicting Overall Readiness for Lifelong Learning of Volunteers Affiliated with a 4-H Youth Development Program in the Southern Region of the United States Model df SS MS Fa Pb Regression 4 2.487 .622 6.937 <.001 Residual 272 24.375 .090 Total 276 26.862 _________________________________________Model Summary______________________________ Model R R2 R2 F df1 df2 Sig. F Cummulative Change Change Change 1 .304 .093 .013 4.027 1 272 .046 a One Way Analysis of Variance b .05 Alpha Level for the Two-Tailed Test of Significance The coefficient values, t values and corresponding significance levels for the independent variables retained in the regression equation predicting overall readiness for lifelong learning scores are presented in Table 40. 134 Table 40 Coefficient Values, Standard Errors, Standardized Coefficient Values, T Values, and Significance Levels for Independent Variables Retained in the Regression Equation Predicting Overall Readiness for Lifelong Learning Score Variable β SE Beta t pa Constant 3.179 .024 2.442 <.001 Format for learning preferred “Web-based/online training” .135 .042 .185 3.178 .002 Yearly net income “More than $100,001” -.184 .072 -.149 -2.541 .012 Marital status “Single never married” -.142 .062 -.133 -2.286 .023 “Divorced” .141 .070 .117 2.007 .046 a.05 Alpha Level for the Two-Tailed Test of Significance The variables excluded from the regression equation and their corresponding t values and significance levels are illustrated in Table 41. Table 41 Excluded Variables, Standardized Coefficients, T Values, Significance Levels, and Partial Correlations for the Regression Equation Predicting Overall Readiness for Lifelong Learning Score Variable Beta In t p Partial Correlation Age as of Last Birthday -.016 -.264 .792 -.016 Length in current employment position -.033 -.558 .577 -.034 Highest level of education completed “Some College” -.097 -1.673 .095 -.101 “Bachelors degree” .039 .678 .499 .041 “Graduate school” .048 .817 .415 .050 Yearly net income “Less than $25,000” -.096 -1.620 .106 -.098 “$25,001 - $50,000” .005 .090 .928 .005 “$50,001 - $75,000” .070 1.200 .231 .073 Marital status “Separated” .093 1.617 .107 .098 “Widowed” .005 .084 .933 .005 Gender .022 .371 .711 .023 Ethnicity .070 1.198 .232 .073 Format for learning preferred “Formal classes” .019 .313 .755 .019 “Workshops” -.031 -.358 .720 -.022 “Mentoring” -.055 -.935 .351 -.057 a.05 Alpha Level for the Two-Tailed Test of Significance 135 CHAPTER 5 CONCLUSIONS AND RECOMMENDATIONS Purpose of the Study The overall purpose of this study was to explore and determine the degree of readiness for lifelong learning of adult volunteers affiliated with a 4-H Youth Development Program in the southern region of the United States. Specifically, the study addressed the following objectives: 1. To describe adult volunteers affiliated with a 4-H Youth Development Program in the southern region of the United States on the following demographic characteristics a) Age b) Gender c) Ethnicity d) Highest educational level completed e) Yearly net income f) Marital Status g) Presence of children at home h) Employment status i) Length in current employment position j) Current occupational category k) Whether or not volunteer’s current employment requires continuous certification l) Number of times respondent has changed jobs in the last five years m) Length of time volunteering n) Format in which respondents prefer learning 136 2. To determine the readiness for lifelong learning of adult volunteers affiliated with a 4-H Youth Development Program in the southern region of the United States as measured by the Readiness for Lifelong Learning Scale 3. To determine if differences exist in the readiness for lifelong learning as measured by the Readiness for Lifelong Learning Scale within the following demographic characteristics a) Gender b) Ethnicity c) Highest educational level completed d) Yearly net income e) Marital status f) Presence of children at home g) Employment status h) Current occupational category i) Whether or not volunteer’s current employment requires continuous certification j) Format in which respondents prefer learning 4. To determine if a model exists which would explain a significant portion of the variance of readiness for lifelong learning as measured by the readiness for lifelong learning overall item mean score and the demographic characteristics of age, gender, ethnicity, highest educational level completed, yearly net income, marital status, length in current employment, and format in which respondents prefer learning. Procedures This study targeted adults who volunteer to a 4-H youth development program. However, the accessible population was adult volunteers whose emails were available from the volunteer 137 enrollment database system of a state 4-H Youth Development Program located in the Southern Region of the United States. The questionnaire used in this study, the Readiness for Lifelong Learning Questionnaire consisted of three sections: readiness to respond to triggers for learning, readiness to overcome deterrents to participation in learning, and self-directed learning readiness. The first two sections were developed by the researcher based on a review of related literature, while the last section was adapted and modified from an existing questionnaire. The questionnaire was reviewed by subject matter experts to establish face and content validity. Feedback was also sought from graduate students in a doctoral level research methods class and members of a church with regards to the necessity, relevance, structure and clarity of items in the questionnaire. The questionnaire was administered via an online survey system (Zoomerang). A total of 1815 adult volunteers who had provided usable emails in the enrollment database system were invited to participate in this study. The final response count was 277 representing a 15.3% response rate. Summary of Major Findings Objective One  Age – The results indicated that the majority of respondents were middle-aged. The two largest groups of respondents indicated their age fell between 36 and 45 years (n = 96, 35%), and 46 and 55 years (n = 91, 33.2%).  Gender – The majority of the respondents reported their gender as female (n = 230, 83.9%) while the remaining 16.1% (n = 44) of respondents indicated their gender as male. 138  Ethnicity – The majority of respondents identified themselves as Caucasians (n = 238, 87.2%). The next largest group identified themselves as African Americans (n = 28, 10.3%).  Highest level of education completed – The majority of the respondents reported completing at least a Bachelor’s degree. Those reporting completion of a Bachelor of Arts or Science comprised the largest group (n = 117, 43.0%), followed by 30.1% (n = 82) who reported having completed a Masters degree.  Yearly net income – The largest number of respondents reported their yearly net income as falling between $25,000 and $50,000 (n = 107, 41.3%). The next largest group (n =70, 27.0%) reported their income as falling between $50,001 and $75,000. The smallest group (n = 19, 7.3%) reported their net yearly income as above $100,000.  Marital status – The majority of respondents reported being married (n = 214, 79.3%). The group featuring the least number of respondents was widowed (n = 8, 3.0%).  Presence of children at home - The largest group of respondents reported having children at home (n = 184, 67.9%). The remaining 32.1% of the respondents (n = 87) indicated they do not have children at home.  Employment status – The majority of respondents reported being employed full time (n = 226, 82.5%). The lowest number of respondents reported being employed on a contract basis (n = 9, 3.3%).  Length in current employment position – The largest group of respondents reported being in their current employment for between 1 and 10 years (n = 111, 43.4%). The next largest group (n = 66, 25.8%) reported being in their current employment for between 10 and 20 years. Three respondents (1.2%) reported as being currently unemployed. 139  Current occupational category – The majority of the respondents reported their current occupational category as professional/managerial (n = 217, 88.9). The current occupational category that had the least number of respondents (n = 5, 2.1%) was trade/labor.  Current employment requires continuous certification or licensure – The largest group of respondents (n = 175, 66.8%) indicated that their current employment requires certification or licensure while the remaining 33.2% (n = 87) indicated their current employment did not require continuous certification or licensure.  Number of times respondent has changed jobs in the last five years – The majority of respondents (n = 191, 72.6%) indicated they had not changed jobs in the last five years. The next largest group of respondents (n = 47, 18%) indicated that they changed jobs only once in the last five years.  Length of time volunteering – The largest group of respondents indicated that they had volunteered with the 4-H Youth Development program for between 1 and 10 years (n = 165, 65.2%). The next largest group (n = 42, 16.6%) indicated they had volunteered for between 10 and 20 years. The average length of time volunteering was 16.1 years (SD = 26.77).  Format in which respondents prefer learning – The largest group of respondents (n = 154, 59.5%) had a preference for workshops. The least preferred format for learning was mail correspondence (n = 8, 3.1%). Objective Two Factor analysis was undertaken for each of the three parts that comprised the readiness for lifelong learning questionnaire. Findings for the self-directed learning part of the survey indicated the presence of one factor, loading all 32 variables, which was named general self- 140 directed learning characteristics. In the scale, respondents rated the extent to which a list of characteristics related to self-directed learning readiness measured a characteristic of themselves. The item that received the highest level of agreement from respondents was “I have high personal Standards” with a mean 3.67 (SD= 0.471) which fell in the “strongly agree” range on the interpretive scale. Overall, the response to most items (27 items) fell within the “Agree” range on the interpretive scale. Findings for the readiness to respond to triggers for learning revealed a three factor solutions that explained 49.79% of the total variance. The first factor labeled “primary changes” loaded 14 variables. These included major, one-time challenges or changes in life circumstances, which would not be classified as everyday occurrences which trigger adult participation in learning. Factor Two labeled “secondary changes” loaded 9 variables which can be classified as everyday changes in life circumstances or regular life maintenance challenges. Factor Three labeled “work changes” loaded 4 variables which were primarily work-related changes in circumstances. Respondents were presented with a list of circumstances likely to occur in an adult’s life and directed to rate the extent to which they would seek and participate in learning activities if the listed events were to occur in their lives. The item that respondents expressed the highest likelihood of seeking and participating in learning activities was “Helping teenagers (children or sibling) become responsible adults” with a mean 3.62 (SD= 0.642) which fell in the “very likely” range on the interpretive scale. Overall, the response to most items (17 items) fell within the “Likely” range on the interpretive scale. Findings for the readiness to overcome deterrents to participation in learning revealed a Four-Factor solution which explained 63.27% of the total variance. The first factor labeled “programmatic issues” loaded five variables. The second factor labeled “dispositional issues” loaded four variables. The third factor labeled “cost issues” loaded three variables while the 141 fourth factor labeled “learning technology issues” loaded three variables. The items in this section of the survey assessed the level of the respondents’ agreement about their readiness to overcome some listed deterrents to participation in learning. The item that received the highest level of agreement from respondents was “I can learn regardless of my age” with a mean 3.54 (SD= 0.512) which fell in the “Strongly Agree” range on the interpretive scale. Overall, almost half of the responses (8 items) fell within the “Agree” range on the interpretive scale, while the rest (7 items) fell within the strongly agree range. The overall readiness for lifelong learning score was obtained by summing the sub-scale scores from the three sections of the Readiness for Lifelong Learning Questionnaire. The item mean score for the overall readiness for lifelong learning (M = 3.198, SD = .31197) fell within the “high readiness” category on the interpretive scale developed for the overall score. Objective Three  Gender - The differences in the overall readiness for lifelong learning score between the gender groups were not statistically significant (F1, 272 = 2.117, p = .147).  Ethnicity – There were no significant differences in the overall readiness for lifelong learning score within the reported ethnic groups (F5, 267 = .710, p = .616).  Highest level of education completed - The differences in the overall readiness for lifelong learning score between groups based on the highest level of education completed were not significant (F8, 263 = 1.193, p = .303).  Yearly net income - The Levenes Test of Homogeneity of Variance revealed a violation of the assumption of equal variances among the groups based on yearly net income (F4, 254 = 3.858, p = .005). A calculation of the Welch Statistic revealed that the differences in the overall readiness for lifelong learning score among the groups reporting the various yearly net incomes were statistically significant (4.742; 4, 78.297; p = .002). From 142 Tukeys post hoc analysis, the difference was found to be between those reporting more than $100,000 yearly net income and those reporting a yearly net income of between $50,001 and $75,000 (mean difference = .25).  Marital status - There were significant differences in the overall readiness for lifelong learning score within the groups based on marital status (F4, 265 = 2.819, p = .026). From Tukeys post hoc analysis, the difference was found to be between who reported being “single/never married” and those who reported being “divorced” (mean difference = -.27).  Presence of children at home - The differences in overall readiness for lifelong learning score between those with children at home and those without were not statistically significant (F1, 269 = .886, p = .347).  Employment status - There were no significant differences in the overall readiness for lifelong learning score within the groups based on current employment status (F4, 269 = 1.918, p = .108).  Current occupational category - The differences in overall readiness for lifelong learning score between the groups based on different occupational categories were not statistically significant (F2, 241 = 1.288, p = .278).  Current employment requires continuous certification/licensure - The differences in overall readiness for lifelong learning score based on whether current occupation requires continuous certification/licensure or not were not statistically significant (F1, 260 = 2.835, p = .093).  Format in which respondents prefer learning - There were significant differences in the overall readiness for lifelong learning score within the groups based on the format in which respondents preferred learning (F4, 265 = 2.819, p = .026). From Tukeys post hoc 143 analysis, the difference was found to be between who reported a preference for “Web-based/online training” learning format and those who reported preference for “Workshops” learning format (mean difference = .141). Objective Four An exploratory model was found to exist that explained a significant portion of the variance in overall readiness for lifelong learning mean score (R2 = .093) from selected demographic variables (F4, 272 = 6.937, p = <.001). Four independent demographic variables retained in the regression equation were found to significantly contribute to the regression model. The variables included “web-based/online training” learning format, “more than 100,001” yearly net income, “single/never married” marital status, and “divorced” marital status. Conclusions, Implications and Recommendations Conclusion One The respondents to this study were predominantly Caucasian (87%), middle-aged (average age was 46 years), female (83%), married (79%), have children (68%), have a Bachelors degree or higher educational level (71%), and are in full-time employment (82%). In some respects this may be typical demographics for 4-H volunteers. Studies by Fritz, Barbuto, Marx, and Etling (2000) and Fritz, Barbuto, Karmazin, and Burrow (2003) found that 4-H volunteers were middle-aged (average age was 46 years), married and most had children in 4-H at the time or had had in the past. One of the main factors that motivated them to be 4-H volunteers was to be with their children. The study Fritz, Barbuto, Marx, and Etling (2000) found that the majority of volunteers had a high school education. Whereas the study provides valuable information about the readiness for lifelong learning of volunteers to this 4-H Youth Development Program, generalizing the results presents a challenge. The results of this study thus apply to a slice of the volunteer population who are 144 Caucasian, female, married and have children, highly educated and employed full time. This is atypical of a general adult population. It is recommended that the study be conducted with a more general adult population which may be diverse with regards to the above mentioned demographic variables. Conclusion Two The results of this study indicated that the 4-H volunteers in this study had an overall readiness for lifelong learning which was categorized as “high readiness”. The implication is that they are more likely to engage in lifelong learning since they ranked themselves high in personality characteristics related to self-directed learning, rated highly the likelihood that they would participate in learning when faced with circumstances known to trigger adult participation in learning, and rated themselves highly when it comes to ability to overcome known deterrents to participation in learning. Due to the exploratory nature of this study, and the fact that this study used a new conceptualization of lifelong learning readiness, there are not studies in the literature to which these results can be effectively compared. However, a look at Desjardins, Rubenson, and Milana’s (2006) study shows that certain demographic variables can help explain these results. The likelihood of participating in adult education varies by many demographic variables with some having more effect than others. For instance, those with higher levels of educational attainment are more likely to participate in adult education. According to Desjardins, Rubenson, and Milana (2006) it is “through formal education adults acquire a readiness to learn” (p. 67). It prepares people for further learning. They also state that employed adults are more likely to participate than unemployed adults. The 4-H volunteers were preponderantly employed full time and indicated high levels of education completed, hence the more likelihood they would indicate high readiness for lifelong learning. 145 The recommendation is that the survey be administered to a more diverse population especially to capture the readiness for lifelong learning of a population with lower formal education attainment (less than bachelors degree) and those with less than full-time employment (unemployed, part-time employed or contract workers). Conclusion Three The first part of the readiness for lifelong learning questionnaire assessed the extent to which respondents identified themselves as possessing characteristics associated with self-directed learning. Overall, responses to most items fell within the “agree” range on the interpretive scale. The implication here is that volunteers to the 4-H Youth Development Program responding to this survey identified in themselves attitudes, abilities and personality characteristics possessed by self-directed learners. This part of the questionnaire was adapted and modified for this study from Self-Directed Learning Readiness Scale (SDLRS) developed by Fisher, King and Tague (2001) which was originally designed to assess nurses’ work-related self-directed learning. This study however failed to reproduce the three factor solution that emerged from Fisher, King, and Tague’s (2001) original study. In this study, the self-directed learning readiness part of the survey was found to have one strong underlying factor. This difference is attributed to the use of the questionnaire with 4-H volunteers who happened to be preponderantly female, highly educated, employed full-time, middle-aged, married, and living in the United States which may be different from undergraduate nursing students living in Australia used in the original study by Fisher, King, and Tague’s (2001). It is recommended that this section of the questionnaire be tested with nursing students in the United States and a general adult population to see if a factor structure close to the original factor structure would emerge. 146 Conclusion Four Results indicated that the respondents were likely to engage in learning when faced with circumstances known to trigger adult participation in learning. Responses to most items in the readiness to respond to triggers for learning part of the survey fell within the “likely” range on the interpretive scale. The triggers for adult learning are likely to occur throughout life (lifelong) and cover breadth of life (life-wide), hence, by responding to these triggers, respondents are likely to engage in lifelong and life-wide learning. After a review of the literature on adult life cycle, Aslanian and Brickell (1980a) concluded that adult life is divided into stages which are rooted in their biological, social and psychological nature. Each stage has its own challenges and opportunities which may be met through engagement in learning. The implication here is that people in different stages of life may be experiencing different triggers for adult learning. It is possible that two people in different life stages may rate one trigger for adult learning differently or respond to it differently. It is thus recommended that in future studies involving triggers for adult learning, a generational effect be investigated. Conclusion Five There were significant differences in the overall readiness for lifelong learning mean score based on marital status, yearly net income and preferred format for learning. The respondents who reported being “divorced” had a significantly higher mean than those who reported being “single never married”. Those who reported a yearly net income of “$50,001-$75,000” had a significantly higher overall readiness for lifelong learning mean score that those earning “greater than $100,001”. Those who reported preference for “web-based/online training” format for learning had a significantly different mean in the overall readiness for lifelong learning score than those who chose “workshops” as a preferred format for learning. 147 The implication is that within this group of 4-H volunteers, those who are divorced were more likely to have a higher readiness for lifelong learning than single people. Volunteers who reported earning between $50,001 and $75,000 were more likely to have a higher readiness for lifelong learning than those earning more than $100,000 yearly net income. Finally, those who reported preference for web-based learning were more likely to have a higher readiness for lifelong learning than those who reported preference for workshops. There was an expectation that there would be significant differences in the overall readiness for lifelong learning score based on such demographic variables as formal education attainment, ethnicity, age and gender which Desjardins, Rubenson, and Milana (2006) list as contributing to differences in participation in adult learning which was not met in this study. The observations above may be due to the specific demographic characteristics of the population studied. It is recommended that this survey be administered to a general adult population to investigate the demographic variables that contribute to differences in readiness for lifelong learning and compared to see whether they differ from demographic characteristics which bring about significant differences with regards to participation in adult education. Also, it is recommended that items addressing ways in which respondents’ source information, with special emphasis on online learning technologies and social media be included in the survey. The role in which online/web-based learning plays in promoting self-directed learning readiness and lifelong learning readiness should be investigated. Conclusion Six A regression model was found that explained a significant portion of the variance in the overall readiness for lifelong learning score with four independent demographic variables significantly contributing to it. The variables included “web-based/online training” learning format, “more than 100,001” yearly net income, “single/never married” marital status, and 148 “divorced” marital status. 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Unpublished Doctoral Dissertation, Trinity International University, Deerfield. 159 APPENDIX A LOUISIANA STATE UNIVERSITY INSTITUTIONAL REVIEW BOARD (IRB) FOR PROTECTION OF HUMAN SUBJECTS APPROVAL LETTER 160 161 APPENDIX B READINESS FOR LIFELONG LEARNING INSTRUMENT 162 Readiness for Lifelong Learning Questionnaire Thank you very much for participating in this survey. Your participation in this survey is voluntary and your cooperation is highly appreciated. Your responses will be kept confidential. The survey has four parts and will take approximately 10-15 minutes to complete. Definition of Learning Activities Adults are increasingly engaging in learning activities for a variety of reasons. 1. For the purposes of this study, only those activities that you engage in for the sole purpose of gaining some specific knowledge, skills or attitudes are considered learning activities. The total amount of time engaged in a specific learning project should exceed two hours 2. Learning activities may include enrollment full-time or part-time in a college or vocational school, attending seminars and workshops within the community, training at places of employment, television courses, independent reading/study projects (library or online), correspondence courses, mentoring, goal-directed informal learning from colleague/friend PART 1 Directions Below is a list of characteristics of adult learners. Please rate each item regarding the degree the item measures a characteristic of yourself (1 = Strongly Disagree-SD, 2 = Disagree-D, 3 = Agree-A, 4 = Strongly Agree-SA) SD 1 D 2 A 3 SA 4 1 I solve problems using a plan 2 I manage my time well 3 I have good management skills 4 I prefer to plan my own learning 5 I am able to focus on a problem 6 I need to know why 7 I critically evaluate new ideas 8 I learn from my mistakes 9 I am open to new ideas 10 When presented with a problem I cannot resolve, I will ask for assistance 11 I am responsible 12 I like to evaluate what I do 13 I have high personal expectations 14 I have high personal standards 15 I have high beliefs in my abilities Please go on to the Next Page 163 SD 1 D 2 A 3 SA 4 16 I am aware of my own limitations 17 I am confident in my ability to search out information 18 I enjoy studying 19 I have a need to learn 20 I enjoy a challenge 21 I want to learn new information 22 I enjoy learning new information 23 I set specific times for my study 24 I am self-disciplined 25 I like to gather facts before I make a decision 26 I evaluate my own learning 27 I prefer to set my own criteria on which to evaluate my learning 28 I am responsible for my decisions/actions 29 I can be trusted to pursue my own learning 30 I can find out information for myself 31 I like to make decisions for myself 32 I prefer to set my own learning goals PART 2 Directions Below are circumstances that are likely to occur in any adult’s life. In each circumstance presented below, please indicate the likelihood that you would seek and participate in a learning activity if it occurs in your life (1= Very Unlikely, 2= Unlikely, 3= Likely, 4= Very Likely). Very Unlikely 1 Unlikely 2 Likely 3 Very Likely 4 1 Moving into a new job 2 Major changes at work e.g. new equipment, new regulations 3 Getting a new major responsibility at work 4 Getting a promotion at work 5 Dealing with a specific immediate task at work 6 Seeing work colleagues get ahead in their careers 7 Entering a new marriage 8 A pregnancy or a pregnant spouse 9 Getting a new baby through childbirth or adoption 10 Dealing with a major conflict with a close family member 11 Need to improve relationships with close family members 12 A close family member dealing with a crisis e.g. substance abuse 13 Need to help children/siblings go through school 14 Helping teenagers (children or siblings) become responsible adults Please go on to the Next Page 164 Very Unlikely 1 Unlikely 2 Likely 3 Very Likely 4 15 A high price expenditure decision e.g. buying a house, car, equipment 16 Reduction in family income 17 Increase in family income 18 Rising cost of living 19 Loss of personal health through injury or illness 20 Injury or illness of a family member 21 Need to maintain good health 22 Moving to a new location e.g. neighborhood, city 23 Acquiring a new house or apartment 24 Retirement 25 Getting a divorce 26 Changes in hobbies 27 Loss of a spouse or close family member 28 Changes in communication technology 29 Changes in information technology e.g. computer programs PART 3 Directions Please rate each item regarding the extent to which you agree or disagree that the item measures a characteristic of yourself (1 = Strongly Disagree-SD, 2 = Disagree-D, 3 = Agree-A, 4 = Strongly Agree-SA) SD 1 D 2 A 3 SA 4 1 I search until I find learning activities that meet my learning needs 2 I am capable of finding good quality learning activities 3 I search until I find learning activities that fit my schedule 4 I always make time to learn when I need to 5 I am able to balance time between family and learning activities 6 I always find a cost effective way to learn what I need to learn 7 I always find ways to cover the costs for the learning that I need 8 Costs cannot keep me from learning what I need to learn 9 I am confident of my learning ability 10 I can learn regardless of my age 11 Age cannot keep me from learning what I need to learn 12 I do what it takes to get ready to learn what I need to learn 13 I am confident in my ability to search for information online 14 I can use technology to access a variety of learning activities 15 I am confident in my ability to use technology in learning Please go on to the next page 165 PART4 Directions Please provide the following information regarding your personal characteristics. This information is intended to better help the researcher analyze the collected data. CONFIDENTIALITY for individual responses is guaranteed. Please select or type your response to the following questions in the space provided. 1. Age as of your last birthday ___________________ 2. Gender Male ___ Female ___ 3. Ethnicity African American Hispanic Asian Caucasian Native American Other Please Specify _______________ 4. Highest Level of Education Completed Less than High School Diploma High School Diploma Some Vocational/Technical School Vocational/Technical School Degree Some College Associate Degree Bachelors Degree (BA/BS) Masters Degree (MA/MS/ MBA) Professional Degree ( J.D./M.D.) Doctoral Degree (Ph.D/Ed.D/Psy.D) 5. Yearly net income Less than $25,000 $25,001-$50,000 $50,001- $75,000 $75,001- $100,000 More than 100,001 6. Marital status Single Never Married Married Separated Divorced Widowed Please go on to the next page 166 7. Do you have children living at home Yes No 8. Employment status Unemployed Employed Full Time Employed on a Contract Basis Employed Part Time Retired 9. Length in current employment (Approximate) Years _____ Months _______ 10. Your current occupational category (Use the Occupational guide provided below) Professional and managerial Sales, service and support Trades Professional and managerial Executive, Administrative, Managerial Occupations Engineers, Surveyors, and Architects Natural Scientists and Mathematicians Social Scientists, Social/Religious Workers and Lawyers Teachers: College, University, and Other Teachers, except Postsecondary Institution Health Diagnosing and Treating Practitioners Registered Nurses, Pharmacists, Dieticians, and Therapists Writers/Artists/Entertainers/Athletes Health Technologists and Technicians Sales, service and clerical Technologists and Technicians, except Health Marketing and Sales Occupations Administrative Support Occupations, including Clerical Service Occupations Miscellaneous Occupations Trade and labor Agricultural, Forestry, and Fishing Occupations Mechanics and Repairers Construction/Extractive Occupations Precision and Production Working Occupations Transportation and Material Moving Occupations 11. Current employment requires continuous certification? Yes No Please go on to the next page 167 12. In the last five years, how many times have you changed jobs? ______ (Number of times) 13. Volunteer role Activity leader/helper Board/committee member Chaperone Fundraising Judge Organizational club leader Project leader Resource person 14. Length of time volunteering (Approximate) Years _____ Months ______ 15. Do you intend to continue volunteering? Yes No 16. Have you been offered orientation or training to help you with your volunteer work? Yes No 17. In what format do you prefer learning? Formal classes Workshops Web-based/online learning Mail correspondence Mentoring Pod casts 168 APPENDIX C SURVEY PRE-NOTICE 169 Dear 4-H Volunteer, In two days, you will receive an email inviting you to participate in a study on adult learning. You are being asked to participate by filling out a 10-15 minute questionnaire. Completion of this questionnaire will help LSU Ag Center 4-H professionals, adult educators and instructors better understand the readiness of adults to engage in learning throughout life (lifelong learning). Your participation is vital to the success of this study. Your participation is completely voluntary. Your responses will remain strictly confidential and you will not be identified in any way in the final reports. Please feel free to contact us if you have any questions about this study. Thank you in advance for participating in this study. Sincerely, Kenneth Kungu Doctoral Candidate School of Human Resource Education Louisiana State University kkungu1@tigers.lsu.edu 225.287.1002 Krisanna Machtmes, PhD Associate Professor School of Human Resource Education Louisiana State University machtme@lsu.edu 225.578.7844 Janet Fox Professor & Associate Department Head 4-H Youth Development Louisiana State University AgCenter jfox@agcenter.lsu.edu 225.578.6751 170 APPENDIX D SURVEY FIRST LETTER 171 Dear 4-H Volunteer In recent years, there has been an increase in studies aimed at understanding adult engagement in learning, especially post-compulsory schooling. I am conducting a study on adult learning that will help LSU Ag Center 4-H professionals, adult educators and instructors better understand the readiness of adults to engage in learning throughout life (lifelong learning). You have been selected to participate in this study because of your volunteer service with the state 4-H Youth Development Program. As a 4-H volunteer and adult learner, your unique perspective and opinions are valuable to this study. Your help is needed by filling out a Web-based questionnaire which will take approximately 10-15 minutes to complete. Participation in this study is completely voluntary and your responses will remain strictly confidential. By completing this survey, you are agreeing to participate in this study. If you have any concerns or questions about your rights as a participant, please contact Robert C. Mathews, Institutional Review Board Chairman, LSU at (225) 578-8692 or irb@lsu.edu. If you prefer responding to a paper-based questionnaire, I would be glad to mail you one if you emailed me your physical address at kkungu1@tigers.lsu.edu. Thank you for your assistance with this study. Feel free to contact us if you have any questions or concerns. Sincerely, Kenneth Kungu Doctoral Candidate School of Human Resource Education Kkungu1@lsu.edu 225.287.1002 Krisanna Machtmes, PhD Associate Professor School of Human Resource Education Louisiana State University machtme@lsu.edu 225.578.7844 Janet Fox, PhD Professor & Associate Department Head 4-H Youth Development Louisiana State University AgCenter jfox@agcenter.lsu.edu 225.578.6751 172 APPENDIX E SURVEY FIRST REMINDER 173 Dear 4-H Volunteer, A Web-based adult learning questionnaire was emailed to you last week. It is for a study aimed at helping 4-H/Ag-Center professionals, adult educators and instructors better understand the readiness of adults to engage in learning throughout life. As a 4-H volunteer and adult learner, your unique perspective and opinions are valuable to this study. Please accept my sincere gratitude if you have already completed the questionnaire. If you have not completed the survey, please do so today by clicking on the link below. It will only take 10-15 minutes of your time. Participation in this study is completely voluntary. Your responses will remain strictly confidential. If you prefer responding to a paper-based questionnaire, I would be glad to mail you one if you emailed me your physical address at kkungu1@tigers.lsu.edu. Please feel free to contact me if you have any questions or concerns. By completing this survey, you are agreeing to participate in this study. If you have any concerns or questions about your rights as a participant, please contact Robert C. Mathews, Institutional Review Board Chairman, LSU at (225) 578-8692 or irb@lsu.edu. COMPLETE THE SURVEY BY CLICKING ON THE LINK AT THE END OF THIS EMAIL. Thank you for your assistance with this study. Sincerely Kenneth Kungu Doctoral Candidate School of Human Resource Education Kkungu1@tigers.lsu.edu 225.287.1002 Krisanna Machtmes, PhD Associate Professor School of Human Resource Education Louisiana State University machtme@lsu.edu 225.578.7844 Janet Fox Professor & Associate Department Head 4-H Youth Development Louisiana State University AgCenter jfox@agcenter.lsu.edu 225.578.6751 174 APPENDIX F SURVEY SUBSEQUENT REMINDERS 175 Dear 4-H Volunteer, Your participation is still needed in a 10-15 minute Web-based adult learning questionnaire. Please accept my sincere gratitude if you have already completed the questionnaire. If you have not, please do so by clicking the link at the end of this email. As a 4-H volunteer and adult learner, your unique perspective and opinions are valuable to understanding the readiness of adults to engage in learning throughout life. Your participation is vital to the success of this study. Participation in this study is completely voluntary and your responses will remain strictly confidential. If you prefer responding to a paper-based questionnaire, please email your physical address to kkungu1@tigers.lsu.edu. Please feel free to contact us if you have any concerns. By completing this survey, you are agreeing to participate in this study. If you have any concerns or questions about your rights as a participant, please contact Robert C. Mathews, Institutional Review Board Chairman, LSU at (225) 578-8692 or irb@lsu.edu. Sincerely, Kenneth Kungu Doctoral Candidate School of Human Resource Education Kkungu1@tigers.lsu.edu 225.287.1002 Krisanna Machtmes, PhD Associate Professor School of Human Resource Education Louisiana State University machtme@lsu.edu 225.578.7844 Janet Fox Professor & Associate Department Head 4-H Youth Development Louisiana State University AgCenter jfox@agcenter.lsu.edu 225.578.6751 176 APPENDIX F PERMISSION TO USE SDLRS QUESTIONNAIRE 177 fromMurray Fisher To Kenneth Kungu Date Tue, Sep 23, 2008 at 6:17 PM Subject RE: self-directed learning readiness scale Kenneth I have attached the scale for your use. Could you please let me know how you intend to adapt the scale for use. Regards Murray Fisher Associate Dean (Academic) Senior Lecturer Faculty of Nursing and Midwifery (MO2) University of Sydney Ph: +61 2 93510587 Fax: +61 2 93510654 Original Message Hello Dr. Fisher, I am writing to request a copy of your self-directed learning readiness scale to consider using it in my dissertation study. I am a doctoral candidate at Louisiana State University’s School of Human Resource Education and Workforce Development. I have began work on my dissertation with my tentative topic being “Assessing Lifelong Learning Readiness”. My goal is to develop an instrument for assessing lifelong learning readiness that incorporates readiness to respond to triggers for learning, self-directed learning readiness and readiness to overcome deterrents to participation in learning. Since self-directed learning readiness has been studied extensively and its measures developed, I was looking to use one already established measure for it. With permission from the author(s) of such a measure, I am looking to use parts of the self-directed learning readiness instrument in the lifelong learning readiness instrument as it would fit my study purposes. I am thus writing to request that you kindly consider sending me your instrument and allow me to use it either in its entire form or if need be in an edited form in the self-directed learning readiness part of the instrument that will result from my study. I will be careful to give credit for all parts of your instrument that I end incorporating in my study, if such permision is granted. Thank you for your kind consideration and any help will be highly appreciated. Regards, Kenneth Kungu, Doctoral Candidate, Louisiana State University, School of Human Resource Education
178 VITA Kenneth Kimani Kungu is the oldest son of Patrick K. Kimani and Alice N. Kimani. He was born in Kisumu City in Kenya. He earned his Bachelor of Arts degree from Egerton University in Nakuru, Kenya, in 2001 and his Master of Science in human resource and leadership development from Louisiana State University in 2005. He also has had professional training in human resource management from the Kenya Institute of Management and management information systems from the Kenya School of Professional Studies. The degree of Doctor of Philosophy will be conferred by Louisiana State University at the May, 2010, Commencement Ceremony. Prior to joining Louisiana State University for graduate studies he had worked with Avenue Group and Aga Khan Hospital, both located in Nairobi, Kenya. He is a member of the African Studies Association, American Association of Adult and Continuing Education, American Educational Research Association, Gamma Sigma Delta Honor Society, and the International Society for Self-Directed Learning.
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