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Course completion and older adult students: The effects of self-efficacy, self -regulation, and motivation
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Course completion and older adult students: The effects of self-efficacy, self -regulation, and motivation
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COURSE COMPLETION AND OLDER ADULT STUDENTS: THE EFFECTS OF SELF-EFFICACY, SELF-REGULATION, AND MOTIVATION by Miki Nakasone Carpenter A Dissertation Presented to the FACULTY OF TILE GRADUATE SCHOOL UNIVERSITY OF SOUTHERN CALIFORNIA In Partial Fulfillment of the Requirements for the Degree DOCTOR OF PHILOSOPHY EDUCATION (EDUCATIONAL PSYCHOLOGY) May 2005 Copyright 2005 Miki Nakasone Carpenter UMI Number: 3180374 INFORMATION TO U SE R S T he quality of this reproduction is d e p en d en t upon the quality of the copy subm itted. Broken or indistinct print, colored or poor quality illustrations and photographs, print bleed-through, substandard margins, and improper alignm ent can adversely affect reproduction. In the unlikely event that th e author did not se n d a com plete m anuscript an d there are missing pages, th e s e will be noted. Also, if unauthorized copyright material had to be rem oved, a note will indicate the deletion. UMI UMI Microform 3180374 Copyright 2005 by P ro Q u est Information and Learning Com pany. All rights reserved. This microform edition is projected against unauthorized copying under Title 17, United S ta te s Code. ProQ uest Information a n d Learning C om pany 300 North Z e e b Road P.O. Box 1346 Ann Arbor, Ml 48106-1346 Dedication This dissertation is dedicated to my mother, Sumi Nakasone Carpenter. Acknowledgement s I wish to acknowledge Dr. Linda Serra Hagedom for her guidance and mentorship throughout the last two years. The course she taught along with Dr. Melora Sundt in the Summer of 2002 was tremendously motivating and helped to jumpstart my efforts to complete my degree. I would also like to thank Dr. Elizabeth Zelinski for her comments and suggestions regarding my dissertation. I also owe a debt of gratitude to Dianne Morris, PhD advisor, who provided consistently helpful and accurate information to help guide me through the PhD program. I am also very appreciative of my friends and family for their understanding, patience, and encouragement during the time I have been in school. In particular, I would like to thank Sumi, James, Robert, and Mike Carpenter; Cindy, John, Jerad, Jacob, and Elijah Anderson; and Jose Luis Olague for their support throughout this project and in so many other aspects of my life. Table of Contents Dedication............................................................................................................ i Acknowledgements............................................................................................ iii List of Tables...................................................................................................... vii Abstract................................................................................................................ viii Chapter 1: Introduction...................................................................................... 1 Background of the Problem................................................................................ 3 Statement of the Problem.................................................................................... 8 Purpose of the Study............................................................................................10 Significance of the Problem................................................................................11 Research Questions..............................................................................................12 Hypotheses............................................................................................................12 Methodology.........................................................................................................13 Assumptions......................................................................................................... 13 Limitations............................................................................................................14 Delimitations........................................................................................................ 14 Definition of Terms............................................................................................. 15 Organization of the Study...................................................................................18 Chapter 2: Literature Review............................................................................. 19 Self-Efficacy Theory........................................................................................... 19 Self-Efficacy and Academic Goals...................................................... 20 Self-Efficacy and Academic Performance............................................ 22 Self-Efficacy, Self-Regulation, and Motivation...................................23 Self-Efficacy and Student Retention......................................................26 Improving Self-Efficacy.......................................................................... 28 Older students in higher education......................................................................31 Educational Intentions of Older Students............................................. 33 Social Support and Older Students........................................................ 37 Barriers for Older Students..................................................................... 38 Institutional Strategies............................................................................. 40 Conclusions............................................................................................................44 Implications............................................................................................................46 Chapter 3: Research Methodology......................................................................48 Introduction............................................................................................................48 Research Questions .......................................................................................... 48 Hypotheses............................................................................................................. 49 Methodology..........................................................................................................49 Research Population.................................................................................49 Research Design....................................................................................... 50 Instrumentation......................................................................................... 51 Data Analysis.........................................................................................................51 Chapter 4: Results................................................................................................ 55 vi Introduction...........................................................................................................55 Description o f the Sample.................................................................................. 55 Multivariate Analysis...........................................................................................69 Summary............................................................................................................... 82 Chapter 5: Conclusions........................................................................................84 Introduction........................................................................................................... 84 Findings.................................................................................................................84 Implications..........................................................................................................86 Future Studies...................................................................................................... 88 References............................................................................................................. 92 Appendix............................................................................................................... 100 List of Tables Table 1. Gender and Age Groups....................................................................................56 Table 2. Ethnicities and Age Groups............................................................................ 57 Table 3. Marital Status and Age Groups........................................................................ 58 Table 4. Number of Children in Household and Age Groups................................... 59 Table 5. Employment Status and Age Groups.............................................................. 60 Table 6. Degree Earned and Age Group........................................................................ 62 Table 7. Highest Academic Degree Desired and Age Groups.................................... 64 Table 8. Degree Difference (Desired-Eamed) and Age Group................................ 65 Table 9. Correlation Matrix of Variables....................................................................... 68 Table 10. Items and alpha coefficients of scales........................................................... 70 Table 11. Statistics: Research Question #1...................................................................71 Table 12. Pair-wise Comparison of Means: Research Question #1....................... 72 Table 13. Multiple Analysis of Covariance: Tests o f Between-Subjects Effects... 74 Table 14. Cross Products to Test for Interaction Effects......................................... 76 Table 15. Multiple Regression Block Entry: Research Question #2.......................... 77 Table 16. Regression Coefficients: Research Question #2....................................... 79 Table 17. Multiple Regression Block Entry: Research Question #3.......................... 80 Table 18, Regression Coefficients: Research Question #3....................................... 81 Abstract This study was a secondary data analysis of the Transfer and Retention of Urban Community College Students (TRUCCS) data set. The purpose of this study was to examine the differences in the level of academic self-efficacy, self-regulation, and intrinsic r otivation for students of three different age groups: younger (under 24), midage (25-39), and older (40 and above). Additionally, this study attempted to determine how these variables along with job related educational intentions predict course completion for students 40 years of age and older. Significant differences were found between the means of age groups in levels of self-efficacy, self regulation and intrinsic motivation. Self-efficacy was found to be higher in younger and midage students than for older students, while both measures of self regulation and intrinsic motivation were higher for older students than for younger students. Predictors of course completion for students 40 years of age and above were determined by conducting two separate regression analyses. In the first regression, predictors of course completion were self regulation, parental socioeconomic status, and being Asian. Negative predictors were being African-American or Hispanic, and owning a home. The second regression analysis indicated that taking English for work, having a degree, and being Asian were predictors of course completion. Variables that negatively predicted course completion were being African-American or Hispanic, owning a horn , and working more hours. Implications for institutions of higher education and suggestions for future research regarding older adult students were also explored. 1 CHAPTER 1 Introduction Since the early 1900’s community colleges have been providing education in areas such as the liberal arts, job-training, and remedial education. A network of public community colleges was formed in the 1960’s, at w! '„h time there were 457 public community colleges that were serving the needs of many baby boomers looking forward to higher education. Presently, there are 1,166 community colleges in the United States. This count increases to 1,600 if all o f the branch campuses are included. These campuses are loosely networked, sharing the goal of creating greater access to students and serving their communities. Despite these shared goals, each community college is distinct and has its own mission statement. In the United States, over half of all undergraduates are educated by community colleges (American Association of Community Colleges, 2003). In the fall o f 1997, total enrollment in community colleges was over five and one-half million. Women made up approximately 58% of this enrollment. Forty-seven percent of these students were aged 24 and under, while students aged 40 and above made up 15% of total enrollment in 1997 (National Center for Educational Statistics, 1999). The California Community College system is the largest higher education organization in this country, and the world. This system today educates over 2.5 million students who have a wide variety of educational and career goals. The California Community College also serves the largest number of low-income and 107 use vcsto*o» f m R H C O R O S RHIJ A.SU A U TH O R IZA TIO N D ear S tudent. W e rcqutM your p articip atio n in an im portant suidy. Die in fo rm a tio n we are gath erin g iro m this p roject w ill he used to im prove c o lle g e teaching and learning and im prove th e student ex p e rien ce in ..orom unitv colleges. It w o u ld b e helpful i f \\v could e x a m in e tecurdv pertain in g to e d u c a tio n a l p re ju ta tin n . dem ographic d u ra ;, ten ch es and co u rse en ro llm e n t info rm atio n alo n g u n it vo u r rcspon>CH to this survey. 1 he fa m ily H duvatiom d R ights an d P riv ac y A ct nt 1974 <F ;F.RPA»p ro v id e s that an ocfuiiitional in stitu tio n ma> not n-lease v o n tu len h ai into rtw ato n ab o u t n student w ith o u t the s tu d e n t’s consent. P lea se provide u s w ith p erm ission to access these p o rtions o! u n it reco rd s w ith the L os A ngeles C o m m unity C o lleg es Your c o n s e n t w ill aU n allow as u> contact you for follow irp research T hank you. (Jtultt S t n u liu c c d o tii Ph.I) A ssociate P ro fesso r At Chair. C om fm iirit} C o lleg e L eadership :i3 740-721* I hereb y a u ih o n ?c the research te a m headed h> Dr. Linda S e m i H agedom tu obtain trom the L os A ngeles C o m m u n ity I o lieg es the reco rd s o f co u rse regis:n;?r>m (he fin a l co u rse grades I receive, info rm atio n from my c o lleg e app licatio n , scores tro m m y assessm ent tests. .,nd o th e r reco rd s directly pertaining to my academ ic ex p e rien ce at the L n s A ngeles C o m m unity C o lleg es This periT m rim i is valid only lo r the pu rp o ses o f the research dcs«.nhed herein. I u n d erstan d that m> nam e an d v th « /n lo fn u ltu it Itiat may id e n tity m e m d n ntuallv w ill not Ise released by the restv.rehers I p ro v id e my p erm issio n freely v. idnv.c coercion n r threat. S tu d en t s S ignature Date Y our full nam e tp h ’ase pnm j t'SC I P1RH •7- • mmmm 2 minority students than any other college or university (American Association of Community Colleges, 2003). The Los Angeles Community College District (LACCD) is the largest community college district in the California Community College system and in the nation. The LACCD was founded in 1931, and its mission is: “to provide quality education at a reasonable price to transfer students; adults needing to learn or upgrade their skills; employers seeking to retrain their workers; and community members interested in life-long learning.” More than three million students have been educated by the LACCD, educating almost three times as many Latino students, and nearly four times as many African-American students than all of the University of California campuses combined. Further, eighty percent of LACCD students are from underserved populations (LACCD, 2003). More than half of all LACCD students are older than 25 years of age, and more than a quarter are 35 or older. Twenty four percent of students are enrolled full time; Seventy-six percent of students are enrolled part-time; and 39% are enrolled in fewer than six units. (Institutional Research and Information, 2002). Additionally, over 50% of all community college students work full-time while attending classes. Thirty percent o f students who work frill time also attend school full time (American Association of Community Colleges, 2003). Therefore, the majority of community college students are enrolled part time, and also working at least part time. The enrollment of older, working students is also expected to continue to grow (LACCD, 3 2003). Given its mission, it will be important for the LACCD to better understand how to accommodate this large group of older, working students. Background of the Problem The nontraditional student may be defined as a student that has delayed enrollment, attends school part-time, works full time, is financially independent, has dependents other than a spouse, is a single parent, or has no high school diploma (Horn, 1996; National Center for Education Statistics, 2002). Using this definition of “nontraditional students,” it has been determined that 75% of undergraduates may fall into this category (National Center for Education Statistics, 2002). Other studies only refer to those students over 25 years old as “nontraditional,” making only the age distinction. With over 50% of students falling into this category, “nontraditional” may actually describe the majority o f students (Donohue & Wong, 1997; Price, 1998). It is clear that the number of older students is growing and they may be returning to school for a variety of reasons. Some of these reasons may include population, societal, and employment trends, as well as the changing learning needs of older adults throughout their lifetimes. Population, Employment, and Societal Trends in Older Adults As adults live longer, the population of older adults rises nationwide. In addition, the “baby-boomers,” those individuals bom between 1946 and 1964, are large in number and are also moving into the ranks o f older adults. As such, from the years 2010 to 2030, the population of individuals in the United States that will be 4 over the age of 65, will grow by 75% to over 69 million (U. S. Department of the Census, 2000). In California, from 1995 to 2025, the population o f individuals that are of traditional working ages (25-64), will increase from 16 million to 23 million (U. S. Department of the Census, 2000). As Americans are living longer and healthier, they are also working longer and changing careers more often. Older adults today will have a greater number of careers in their lifetimes than ever before (Shek, 2003; Administration on Aging, 2002). Additionally, adults over the age of 65 are experiencing lower rates of disability. From 1989-1994 the disability rate decreased an average of 1.6% per year. During the period between 1994 and 1999, disability among older adults decreased an average of 2.6% per year (National Institutes of Health, 2001). These lower rates of disability will also allow older adults to work longer. There are societal trends that are occurring that may also affect the educational needs of older adults. We know that adults today are living and working longer, but they also have fewer children and enjoy greater self-sufficiency. They also have more technical careers, which require continuously updating their skills (Allen, 2002; Wolf, 1997). All of these population, employment and societal factors impact the needs of older adults, and the demands that will be placed on higher education institutions in the future. 5 It has been suggested that older adults of today require a new model o f lifelong learning that allows them to integrate the various aspects of their lives. The resulting model would include the acceptance and accommodation of a life course that does not impose a chronological order. In the past, this order may have been education early in life, work throughout midlife, and leisure in older adulthood. Adults of today and in the future may choose to experience all three of these aspects of life throughout their live. This may require educational institutions to offer programs that are not only age-appropriate, but considerate of the differing needs of the older student (Kerka, 2001; Kussrow, 2002; Settersten & Lovegreen, 1998). Concerns o f older students Older students have a variety of concerns regarding their educational experiences, in particular, course times and schedules. Many older students also work and require courses that are offered at varying times of the day. Many students have a conflict between work and class times and higher education institutions may better serve these students by evaluating the best times to hold classes for their students (Sissel et al., 2001; Hagedom et al., 2002). This concept also applies to the availability of services at times that are convenient for students. The hours of administrative offices may need to be re evaluated so that students may be able to better conduct the business of registration, buying books, visiting libraries, receiving counseling, and paying bills. Further, many college communications, including photos in school catalogs and web pages, 6 are geared toward younger students. Of even greater significance are the program requirements that higher education institutions have, such as time limits and prerequisites, that are not necessarily adaptable to the older student’s situation (Sissel et al., 2001). On-campus services including counseling and tutoring that is appropriate for the non-traditional student are another concern, since many may be in need of academic support (Stem, 2001; Kerka, 2001; Hagedom et al., 2002). Alternative education programs such as distance learning, may be especially helpful to the older student (Home, 1998). In addition, the hierarchical culture in many higher education institutions may not be appealing to the older student. Older students may respond better to institutions that are less condescending and when they are treated as adults by faculty in the classroom, and by staff outside the classroom (Wlodkowski & Kasworm, 2003). Retention rates and older students One of the difficulties in measuring the success of students, and in particular, older adult students, is that the measures o f success for students nationally, statewide, and locally are not consistent. Measures that are typically used include persistence and retention rates. Persistence is a measure that describes the student’s tendency to stay in school until a desired goal is met, typically degree attainment, which tends to be an assumed goal for students by many institutions. Retention is another measure used by institutions to identifying whether enrollments are 7 maintained throughout a specified term. (National Center for Education Statistics, 2002; California Community Colleges Chancellor’s Office, 2003; Institutional Research and Information, 2002). In 2002, the average persistence rate in the United States was 36%. Persistence in this case was measured by actual degree attainment at any higher education institution in the United States. When traditional and non-traditional students were singled out, persistence was 53% and 27% respectively (National Center for Education Statistics, 2002). In the state of California, the retention rate was 82%, measured by the number of enrollments at the end of a term divided by the number of enrollments at the beginning of the term. However, the state also measures the success rate. This rate is a calculation of the number of enrollments with passing grades divided by the number of enrollments at the beginning of the term. The success rate in California in 2003 was reported to be 67% (California Community Colleges Chancellor’s Office, 2003). Retention in the Los Angeles Community College District for a single term, was reported to be approximately 74% in 2003 (Institutional Research and Information, 2002). When measuring persistence over a period of one year, the rate drops to below 50%. More specifically, persistence rates were 58% for students under 20 years of age, as compared to 35% for students 35 years of age and older. The lowest persistence rates were for black students over 25 years old, with the goal 8 of obtaining basic skills. The students exhibiting the highest level of persistence were Asians under 20 years old that had the goal of transferring to another institution (Institutional Research and Information, 2002). The data above indicate that older, nontraditional students generally exhibit lower retention and persistence rates than younger students. Some research actually suggests that older students may have higher attrition rates, yet may receive higher grades than their younger counterparts (Donohue & Wong, 1997). More research may be necessary to further examine the issue of older adult retention and their higher risk of attrition (Summers, 2002). Often, interventions aimed at students who are at higher risk of attrition, often do not take the initial and/or changing goals of students into consideration (Summers, 2002). More research is needed to not only determine the factors that may affect the retention and persistence of older adult students, but to also consider whether these measures are appropriate for these students as well. Statement of the Problem Retention rates have become increasingly important for community colleges as a measure of evaluating institutional effectiveness and/or accountability. This is due to increasing competition for public resources and decreased funding at the state level (Summers, 2002). The issue of retention is complicated by many factors. It has been suggested that that the educational intentions of older students are important in determining the success of students as they may leave school once their 9 needs are met, regardless if a degree is obtained, or a term is over (Kerka, 1989; Voorhees & Zhou, 2000). Educational intentions for older students may be more complex than just a desire for a degree. Older students tend to persist longer when the program of study is considered highly useful to them, whether or not it includes obtaining a degree (MacKinnon-Slaney, 1994; Peterson & delMas, 1996). Additionally, with increasing numbers of students doing a “shuffle” between various educational institutions and reverse transfer students enrolling in community colleges after already obtaining credits at a four-year college, the issue becomes even more clouded (LeBard, 1999; Maxwell, W., Hagedorn, L. S., Brocato, P., Moon, H. S., & Perrakis, A., 2002). As the enrollment of older adult students continues to rise, retention will be an important issue (Institutional Research and Information, 2002). Further examination is needed to determine whether the lower retention rates for older adult students are accurate measures of the success of students and the effectiveness o f an institution. In addition to educational intentions, the level of academic self-efficacy, self regulation, and intrinsic motivation also play a role in student success and persistence. Research supports the positive impact of academic self-efficacy and intrinsic motivation on self-regulated behavior and academic performance (Byer, 2001; Chemers, Hu, & Garcia, 2001; Deci, Koestner, & Ryan, 2001; Jackson, 2002; Klein, 2002; Lee & Spizer, 2000; Luzzo, Hasper, Albert, Bibby, & Martinelli, 1999; Perry, Hladkyj, Pekrum, & Pelletier, 2001). It is difficult to understand however, 10 how these factors impact older students, as most of the research in these areas involve younger, “traditional” students under the age of 24, or use such wide age categories for older students, that it may be difficult and/or inappropriate to generalize to a population of students that are 40 years of age and above (Byer, 2001; Carney-Crompton & Tan, 2002; Chemers et al., 2001; Donohue & Wong, 1997; Eppler & Harju, 1997; Jackson, 2002; Laanan, 2000; Lee & Klein, 2002; Luzzo et al., 1999; Perry et al., 2001; Spizer, 2000; Voorhees & Zhou, 2000). With the growing numbers of older, nontraditional students enrolling in community colleges, it will be important to understand how community colleges can facilitate greater success in older students. Determining the effects of cognitive- behavioral variables (such as self-efficacy, motivation, and self-regulation) on student success may also be important to consider, to provide better learning opportunities for older students. Further, understanding the educational intentions of older students may be necessary to determine more appropriate measures of persistence (Brawer, 1996). Purpose of the Study Academic self-efficacy, self regulation, and intrinsic motivation have been shown to positively effect academic performance in younger students (Byer, 2001; Chemers et al., 2001; Deci, Koestner, & Ryan, 2001; Jackson, 2002; Lee & Klein, 2002; Luzzo et al., 1999; Perry et al., 2001; Spizer, 2000). It has also been demonstrated that educational intentions and level of academic self-efficacy, both 11 play a role in student success and persistence (Chemers et al., 2001; Peterson & delMas, 1996). As mentioned previously, more than 15% o f the students that are educated by the LACCD are over the age of 40, and this enrollment of older students is expected to continue to grow (LACCD, 2003). The purpose of this study was to determine how the levels o f r elf-efficacy, self-regulation, and intrinsic motivation vary for students of various age groups and what effect these variables have on course completion for older students. Further analysis was conducted regarding how job related educational intentions effect course completion as well. Significance of the Problem The LAC CD’s mission includes educating nontraditional students, including those who are attempting to improve job skills; those who are life-long learners or those who have postponed college under various circumstances (LACCD, 2003). While the LACCD and other community colleges share a similar mission, these colleges need to maintain an awareness of the concerns, desires, and issues of nontraditional adult students. Further, it is unclear as to whether what improves academic performance for younger students will also be effective for older students. It will become increasingly important to understand these differences and design programs that more appropriately support and attract nontraditional students. This is particularly important for community colleges, as this focus will improve the situation for students, and may also help the college to better fulfill its mission (Kasworm, 2003). A new model for lifelong learning must be adopted. There is a 12 need for modifying existing infrastructures and models that will more appropriately meet the needs of lifelong learners (Kussrow, 2000). Research Questions 1. How do the levels of academic self-efficacy, self-regulation, and intrinsic motivation differ among students of various age groups? 2. How is course completion affected by academic self-efficacy, self regulation, and intrinsic motivation for students 40 years of age and older? 3. What types of educational intentions predict course completion for students 40 years of age and older? Hypotheses 1. The level o f academic self-efficacy, self-regulation, and intrinsic motivation of students 40 years of age and above will be higher than students under 24 years of age. 2. Academic self-efficacy, self-regulation, and intrinsic motivation will predict course completion for students 40 years of age and older. 3. Educational intentions related to enhancing job skills will predict course completion for students 40 years of age and above. 13 Methodology This study was conducted utilizing a secondary data analysis of the TRUCCS (Transfer and Retention of Urban Community College Students) data set. The TRUCCS data consisted of survey data collected from students and faculty and LACCD transcripts and records for 5,000 students. The TRUCCS study consisted of data collected by questionnaire. Students also consented to provide access to their confidential transcript data. For this study, the questionnaire data were used to obtain basic demographic and background information on students, as well as to determine levels of self-efficacy, self-regulation, intrinsic motivation, and job related educational intentions. Transcript data was utilized to determine the course completion rate for each student. Course completion rates in this study were measured by taking the cumulative number o f courses that a student had passed in the LACCD, divided by the number of courses that had been attempted by the student. Assumptions For this study, the following assumptions were made; 1 . The measures were reliable and valid indicators o f the constructs to be studied. 2. The purposes, processes, and elements of the framework studied have a degree of applicability and generalizability to urban community colleges throughout the country. 14 3. The LACCD may be representative of other urban community colleges, with similar demographics. 4. The primary data were accurately recorded and analyzed. 5. The participants were assessed in a controlled atmosphere. 6. The participants responded to the best of their ability. Limitations 1. This study was limited to the data that was previously collected from subjects who agreed to participate voluntarily. 2. It was limited to the number of subjects in the TRUCCS data set and the amount of time available to conduct this study. 3. Validity of this study was limited to the reliability of the instruments used. Delimitations The study confined itself to data from the TRUCCS database and the LACCD transcript data for consenting participants only. This study focused on the self-reported responses from the TRUCCS survey to operationalize the variables of self-efficacy, self-regulation, intrinsic motivation, and educational intentions. Only the TRUCCS transcript data was used to assess course completion. 15 Definition of Terms Traditional Students The “traditional student” is a student that is under 24 years of age, enrolled in college immediately following high school, and their primary role is that of a student (Dill & Henley, 1998). Nontraditional students The “nontraditional student” may be defined as one who is over 24 years of age, graduated from high school a year or more prior to college enrollment, and/or possesses multiple roles, including that of parent or employee, in addition to being a student (Dill & Henley, 1998). Some studies in this literature review only make the age distinction when using the term “nontraditional” (Donohue & Wong, 1997; Price, 1998). Younger students In this study, the term “younger students” refers to those who are under 24 years of age. Middle-Aged students “Middle-aged student” refers to students who are 25-39 years of age. Older students “Older students” refers to students that are 40 years of age and older. 16 Academic Self-efficacy The degree of confidence that a student has that he or she will succeed in their educational pursuits (Bandura, 1977). A self-efficacy scale was developed for this study that will be introduced in later chapters. Self-Regulation Self-regulation may be defined as a process that occurs in varying levels in students that causes them to generate goals and learning strategies that may help lead them to accomplish an academic goal. Some of these strategies may include: planning, monitoring, and reflection. (Bandura, 1986; Pajares, 2002; Pintrich & DeGroot, 1990; Zimmerman, 2002). A self regulation scale was developed that will be discussed in later chapters. * Intrinsic Motivation Intrinsic motivation refers to the drive within an individual to engage in a task just for the sake of learning. Performing a task well is its own reward to the individual who is intrinsically motivated. This differs from extrinsic motivation, in which the desirable outcome may be praise, an award, or the avoidance of a punishment. When one is extrinsically motivated, the task may be viewed as a means to an end (Pintrich & DeGroot, 1990; Zimmerman, 2002). A scale of intrinsic motivation was developed for this study, and will be presented in later chapters. 17 Educational Intentions The goal that a student has while enrolled in college measured at a point in time. For the purposes of this study, intentions are defined by the participants’ questionnaire responses regarding the reasons that influenced them to attend their college. These reasons included: wanting to get a better job, learning English for work, and taking a program/certificate needed for their work. Retention An institutional measure that may be determined by calculating the number of students that are enrolled at the beginning o f a term divided by the number of students attending classes at the end of the same term (Institutional Research and Information, 2002). Course completion The ratio of courses passed to courses attempted. This will measure student persistence in this study and will be obtained from the LACCD transcript data. 18 Organization of the Study The first chapter of this dissertation presents the introduction, background of the problem, statement of the problem, purpose of the study, significance o f the problem, research questions, methodology, assumptions, limitations, delimitations, definition o f terms, and the organization of the study. Chapter two is a review of recent literature in the areas o f self-efficacy theory, self-regulation, motivation, and retention. A second section in the literature review examines research in the areas o f older students’ educational intentions, social support, barriers, and institutional strategies aimed at improving the retention of older students. Chapter three presents the methodology used in the study, including a description of the sample, the data collection procedures, a description of instrument development, and the methods of analysis o f the data. Chapter four reviews the results of the study and addresses the research questions of the study. Finally, chapter five outlines the implications of the current study and proposed directions for future research. 19 Chapter 2: Literature Review The purpose of this literature review is to provide an overview of the key variables of this study and to provide the appropriate background information. This literature review will include a summary of self-efficacy theory, including its relationship to academic goals, self-regulation, intrinsic motivation, and retention. Interventions designed to improve levels o f self-efficacy will also be discussed. Since this study is primarily focused on students that are over the age of 40, research will be reviewed related to older students including: educational intentions, social support, and barriers. Finally, institutional strategies aimed at improving the retention of older students were addressed. Self-Efficacy Theory The mere knowledge of how to complete a task is not the equivalent o f believing one can accomplish that task and complete the task successfully. Bandura (1977) refers to self-efficacy as the belief that one can successfully perform a task. Self-Efficacy Theory states that an individual’s perceived efficacy in a particular area will determine whether or not the individual will initiate coping behavior, the amount of effort that will be expended, and how long the individual will persist in the presence of obstacles (Bandura, 1977). Self-efficacy may be described as a link between knowing how to do something and actually doing it. Since self-efficacy is defined as being task specific, one may have varying levels of self-efficacy a ‘ pending on the task at hand. 20 Bandura further describes a model of four basic ways that individuals learn about their ability to perform specific tasks, which may then effect one’s level of self-efficacy. These include performance accomplishments, vicarious experience, verbal persuasion, and emotional arousal. Performance accomplishment may lead to an increase in self-efficacy after successfully performing a task. Additionally, observing others perform an activity may also arouse self-efficacy through vicarious experience. Bandura further explains that being told that one can perform a task successfully may provide verbal persuasion which will increase self-efficacy. Finally, emotional arousal refers to the way an individual may feel in the face of a stressful or taxing situation. This arousal can have an effect on the expectations one has regarding their ability to perform a task. For instance, if one is aroused to feel relaxed and confident, then self-efficacy may be improved. Self-efficacy beliefs are developed within an individual and may change as one pursues their goals. It may also be important in determining whether an individual attempts to exert control over their environment to meet this goal. This control, may be considered self-regulation, which may help an individual become self-directed in which the student generates goals and learning strategies that will lead them to their goal. Some o f these strategies may include: planning, monitoring, and reflection (Bandura, 1977; Zimmerman, 2002). Self-efficacy and academic goals. The level of academic self-efficacy an individual has may also determine the academic goals that one sets for themselves as 21 well as the self-regulating study strategies that they may use. As Pajares describes, students who believe that they are capable of succeeding in a particular task, will set goals, use more strategies and persevere longer (Pajares, 2002). The way that one evaluates goals may also affect self-efficacy and subsequent self-regulation. There are important aspects o f goals that may determine how an individual thinks about those goals which may in turn affect self-efficacy and their self-regulating behavior such as planning and monitoring their behavior. These aspects of goals include specificity, proximity and difficulty (Schunk, 2001). The specificity of a goal may include performance standards that would likely inspire an individual to perform better. Goals that specify the required amount of effort to succeed and provide a clear measure to determine success are more effective in increasing self-efficacy and self-regulation. Goals that are too easily attained are not as effective at maintaining self-efficacy (Schunk, 2001). Schunk describes the proximity of a goal, or how far into the future a goal is projected, can result in higher or lower motivation. Short term goals can result in higher self-efficacy than long term goals, however if long term goals are divided into mid and short term goals, self-efficacy may still be increased due to more frequent self-evaluations. The difficulty level of a goal may also greatly affect an individual’s level of self-efficacy. Goals that are too easily obtained and those that are too difficult to obtain, may both decrease self-efficacy. Goals that are moderately 22 difficult, yet still seem attainable may be the best to motivate and positively effect self-efficacy and self-regulated behavior (Schunk, 2001). Self-efficacy and academic performance. The relationship between self- efficacy and academic performance was examined in a study by Lee and Klein (2002), in which 134 undergraduate students filled out questionnaires at two points in time, just prior to an exam. The survey measured student self-efficacy, self- deception, conscientiousness, and basic demographic information. The student’s performance on the exam was the measure of learning in this study. It was found that self-efficacy and learning were significantly and positively related at both testing points. Early self-efficacy was also positively correlated with conscientiousness. This study may give an indication about the effect of self-efficacy on learning, however the time period between testing for self-efficacy and taking the exam was only 4 weeks. This may not be sufficient to determine how self-efficacy may effect the student’s performance over a longer term. Also, the mean age of students in this study was 21.3 years. Therefore, it is difficult to determine whether these findings hold true for older students as well. Spizer (2000) examined differences in self-efficacy between traditional and nontraditional aged students that attended a private liberal arts college. There were 355 full-time students in this study, with 267 traditional aged students, and 88 nontraditional-aged students. The researcher defined traditional age as 23 years and below, while nontraditional age was determined to be 25 years old and above. This 23 study included a measure o f self-perception, which encompassed academic self- efficacy, social acceptance, and self-worth. Measures of career decision-making self-efficacy, perceived social support, motivation and learning strategies were also administered. Grade Point Averages (GPA) for the two most recent semesters were used to determine learning by students. As Spizer hypothesized, GPA was predicted by higher self-efficacy, self-regulation, and social support. This was the case for all students. Female students that measured high in self-regulation and being a nontraditional student also predicted having a higher GPA. Those students who showed higher social acceptance and self-worth had lower GPA’s overall, however, social support also positively affected GPA. This study went beyond other studies to determine differences between age groups, but still only designated two groups with a nontraditional group that was 25 years of age and over. Unfortunately this very large age range may not allow for inter-group age differences to be seen. Self-efficacy, self-regulation, and motivation. Self-efficacy has been shown to influence both motivation and a student’s use of self-regulated strategies for learning (Bandura, 1986; Pajares, 2002; Pintrich & DeGroot, 1990; Zimmerman, 2002). Older students may be more intrinsically motivated than younger students, and older students may use more advanced study strategies (Justice & Doman, 2001). These self-regulated learners have been shown to actually adapt their use of learning strategy to fit various situations (Wolters, 1998). Self-regulation has also been described as a way for students to compensate for any individual differences in 24 learning and an important aspect in the development of lifelong learning skills (Zimmerman, 2002). In turn, self-regulating strategies that are effective for students may lead to even higher levels of self-efficacy and subsequent achievement (Pajares, 2002). Further, it has also been shown that students with higher levels of self- efficacy may influence the persistence a student has to maintain high achievement (Lent, Brown, & Larkin, 1984; Pajares, 2002). Perry et al. (2001) reported that academic performance, as measured by the final course grade, was positively related to several measures including: academic control, intrinsic motivation, self-monitoring, and perceived control. In this longitudinal study, the researcher collected data at the beginning and end of an academic year. Preoccupation with failure, course anxiety and boredom were negatively correlated with final grades. Those students that had higher perceived levels of academic control demonstrated greater motivation, used self-monitoring strategies more, and felt more in control over their course assignments and life overall, and not only perceived that they would receive higher grades, but did receive higher grades for the course (Perry et al., 2001). Donohue and Wong (1997) reviewed the research suggesting that students who persist have more motivation and are more involved in college activities than those students who do not persist. The authors conducted a study to determine the relationship between motivation and satisfaction in college among both traditional (under 25 years of age) and nontraditional (25 years and older) students. One- 25 hundred and twenty six undergraduate students ranging in age from 19-57 were included in the study. Measures included the College Student Satisfaction Questionnaire, which measures satisfaction with college including the physical conditions, results of academic efforts, instruction and social life. A second measure was the Work and Family Orientation Questionnaire. This instrument is designed to measure achievement motivation including the need to work hard, intellectual challenge, and desire to succeed competitively. This study indicated that there was a positive relationship between satisfaction in college and achievement motivation for both groups. The nontraditional group however, showed a work orientation that was higher than that of the traditional students. Further, for nontraditional students, academic performance (GPA) was negatively associated with measures of competitiveness. Results from this study indicate that nontraditional students may perform better when the emphasis is not on competition and that these students may be willing to work harder than traditional students (Donohue & Wong, 1997). Eppler & Harju (1997) studied achievement motivation goals and academic performance in both traditional (17-21 years old) and nontraditional (22 - 53 years old) students. Goal orientation scores for both learning (intrinsic) and performance (extrinsic) goals were obtained using a self-report inventory. Academic performance was measured by GPA. Both groups of students rated their educational learning goals higher than performance goals, however the nontraditional group rated learning goals higher than traditional students. Academic success was better predicted by 26 learning goal orientation rather than the students’ traditional/nontraditional stalls (Eppler & Harju, 1997). Self-efficacy and Student Retention. Experiences prior to college as well as characteristics o f the institution may affect how well students adjust to college life and persist in an education program (Bean, 1982; Cabrera, Castaneda, Nora, & Hengstler, 1992; Kennedy, Sheckley, & Kehrhahn, 2000; Tinto, 1975). Additional research has shown that higher credit loads, grade point averages, receiving encouragement to stay enrolled, and a commitment to do well in college is associated with greater rates of persistence (Okun, Benin, & Brandt-Williams, 1996). Peterson and delMas (1996) studied persistence issues for a group of nontraditional students from a developmental education unit of a university in the Midwest. Students ranged in ages from 18-48, and most had been out of school for some time or had grade point averages in high school below 2.0. Researchers referred to the sample as under-prepared students and measured Career Decision- Making Self Efficacy (CDMSE) and institutional integration. CDMSE measures the confidence that an individual has in their ability to engage in educational and career planning and decision-making. The Institutional Integration Scale measured social and academic integration and goals and commitments. Petersen and delMas (1996) found that the perceived usefulness of the degree program positively affected persistence for this group of nontraditional students. The authors suggest that nontraditional students may be mure likely to persist if they believe that the degree 27 program will result in better employment opportunities. Further, this study demonstrated that nontraditional students that had higher career decision-making self-efficacy, were more likely to be better integrated both academically and socially. This integration, the authors assert, may in turn cause students to persist in their program of study. Chemers, Hu, and Garcia (2001) conducted a longitudinal study of freshmen college students and examined the relationship between self efficacy and optimism on academic performance, stress, health and retention. Several measures were taken at the end of the students’ first quarter. These included: GPA in high school, academic self-efficacy, optimism, expectations of academic performance, social adjustment, stress and illnesses, and coping strategies. Measures that were taken at the end of the school year included all of the previous measures except for self- efficacy and optimism, but included faculty ratings of course performance. Self- efficacy and optimism, was found to be strongly related to academic performance, and personal adjustment. Self-efficacy was also indirectly related to academic performance through academic expectations, which included the student’s perception of continuing their enrollment in school. Further, Chemers et al. noted that students who had higher expectations of performance actually performed better regardless of past performance (high school GPA). This study was conducted at a 4-year university and 98% of the sample was 18-19 years old. Again, this study may be helpful to better understand student retention, but more research needs to be 28 conducted with older participants. Also, in future studies it would be helpful to utilize a more objective measure of retention. In this study, it was the student’s perception of continuing enrollment and not institutional retention that was measured. The study did however follow students over the course of an academic school year, which is helpful to understanding student retention over a longer period than many other studies examined here. Improving self-efficacy. As mentioned previously, Bandura (1977) suggested that there are four basic ways that individuals learn about their ability to perform specific tasks: performance accomplishments, vicarious experience, verbal persuasion, and emotional arousal. Few studies are available on self-efficacy enhancing programs for older students. Studies that do exist for younger students, are reviewed here, and may still shed some light on the types of strategies that may also apply to older students. The interventions utilized in the Luzzo, Hasper, Albert, Bibby, and Martinelli (1999) study focused on two of the types of learning described by Bandura: vicarious learning and performance accomplishments. The interventions in this study were intended to enhance math/science self-efficacy for undergraduate students. Students were randomly assigned to four treatment conditions that included: the control group, vicarious learning only, performance accomplishments only, or a combined treatment of vicarious learning and performance accomplishments. Vicarious learning in this study involved video presentations of college graduates who were 29 undecided in college, but later had successes in their classes and subsequent careers. Performance accomplishments involved giving participants a number series task (designed to be relatively easy). Students were told that this test measured their mathematical abilities. All participants received a passing score on the task. The combined treatment group received both the video presentation as well as the number series task. The control group did not receive any of these treatments. Luzzo et al. (1999) reported that both vicarious learning and performance accomplishments separately helped to increase students math/science self efficacy beliefs, however, performance accomplishments had a significantly greater effect on self-efficacy measures than vicarious learning alone. The combination of both vicarious learning and performance accomplishments had the greatest effect of all. Additionally, the performance accomplishment treatment group continued to have higher self-efficacy levels even 4-weeks following the treatment. This effect was not seen for the vicarious learning only group, which indicates that this strategy alone, may be insufficient to produce long term changes in self-efficacy (Luzzo et al., 1999). This study is helpful to understand how various interventions may help to increase self-efficacy if they emphasize multiple strategies for learning. This study utilized participants whose ages ranged from 18-23. More research needs to be conducted that also explores ways to improve self-efficacy for older students. Jackson (2002) studied the effect of persuasion and emotional arousal on self- efficacy utilizing electronic mail. In this study, rather than the use of verbal 30 persuasion, the communication took the form of an electronic mail message. Messages that were sent to students in a college course were intended to effect self- efficacy beliefs. Students were divided into three categories of performance: above average, average, and below average based on exam scores. Halfway through the course, students were instructed to send an electronic mail message back to the instructor. Those students that sent an email to the instructor, were randomly assigned to: 1) receive an email that was designed to enhance self-efficacy; or 2) to receive an email that was a neutral reply. The reply that was intended to increase self-efficacy included reminding the student of their prior successes; describing other students similar to them who have successfully completed the course; verbal persuasion to do well; and stress reducing techniques. Self-efficacy was found to be positively related to exam scores at baseline and following the treatment. Also, students who received the encouraging electronic mail had higher scores on the second exam than those who received the electronic mail message that was neutral. This study also had nearly twice as many women as men participating with the mean age o f 21.5 years. The relationship between the college classroom environment and self- efficacy was examined by Byer (2001). In this study, self-efficacy referred to overall academic self-efficacy beliefs. One hundred and two students were randomly selected from college courses at a southeastern university. The Student Assessment of Teaching and Learning (SATL) was used to measure students’ perceptions 31 regarding the classroom environment. There are four sections of this instrument that measured perceptions of classroom involvement, knowledge gained, attainment of higher order thinking skills, and attainment of professional skills. The Personal Learning Efficacy Measurement (PLEM) was used to measure academic self- efficacy. Additionally, the final course evaluations were used to assess the students’ perceptions o f teaching and the effect of the course on their learning. All measures were administered during the second week of classes. The variable that had the strongest relationship with self-efficacy was the students’ perceptions of their classroom involvement (Byer, 2001). The researchers did not differentiate between age groups. Again, as in previous studies, more investigation will need to take place to determine whether this type of intervention may be successful in increasing self- efficacy for older students Older Students in Higher Education In 1997, one-third o f students at community colleges were 30 years of age or above, with students aged 40 and above making up 15% of total enrollment (Howell, 2001). In the LACCD, over one-forth of students are over the age o f 35 (LACCD, 2003). While full time enrollment for these adults has remained fairly stable, part time enrollment of students over 40 has steadily increased since 1993 (National Center for Educational Statistics, 1999). Bryant (2001) reported that part-time enrollment has accounted for 64% o f student enrollments in 1997, with older students making up a larger percentage of these students. 32 Many students may be attending part-time due to other commitments in their lives. There are many factors that may influence a student’s experience in college. Some of these factors may include: purpose to attend college, family responsibilities, and employment issues. This section of the literature review will examine various aspects of the older adult student including: educational intentions, social support, barriers to success, and institutional strategies to meet the needs of older students. Settersten and Lovegreen (1998) explored the nature of educational experiences across the lifespan. The authors described how the life course has been rigidly structured in terms of education and life experiences. Yet, it was proposed that the life course may be more flexibly structured in today’s society. The authors review research that described the “tri-partition” of the life course. This “tri partition” refers to the split of the life course into three distinct phases in which education, work and leisure fit rigidly into separate phases of life. The authors describe how societal, economic, and governmental changes may contribute to the development of a more flexible life course. This would result in the acceptance of life events at different times even though they traditionally occur in specific phases of life. An example of this may include an adult who returns to school later in life for job security and mobility; or the woman who chooses to have a child later in life, but then returns to school. Further, with the life span widening, individuals now have more years to spend in roles in which they work, have families, retire, and learn (Settersten & Lovegren, 1998). 33 In the 1990’s there was much discussion about finding the “balance” in work and life. Individuals were encouraged to develop skills in communicating and family/career management. Kerka (2001) suggests that adult education should focus on transformation and encouraging individuals to integrate their learning by developing skills in four areas including: communication, decision-making, interacting interpersonally, and lifelong learning. Kerka described this framework as Equipped for the Future (EFF). This type of transformative learning requires rethinking assumptions and re-evaluating life roles. While “balance” assumes choosing one aspect of life over another or switching back and forth between roles, the EFF framework implies that all of the roles and activities of an individual need to be merged into an integrated whole. Older students tend to be more interested in this integration or relating new learning to their current level of knowledge. Further, there may be many reasons that older adults return to school. These students may be attending for a variety of reasons including: 1) a desire to keep up with technology; 2) the trend to change careers more often; and 3) older adults much beyond their 30’s are living much longer and continue to stay active physically and mentally (Allen, 2002). Educational intentions of older students. In 2003, the LACCD identified the following educational goals of students: Vocational (38.0%), Transfer (27.2%), General Education (9.7%), Transitional (6.2), and Undecided (8.9%) (LACCD, 2003). Educational intentions are reported by students at the beginning of their 34 enrollment. Vocational students are those that are preparing for a new career, advancement in a current occupation, or to maintain current licensure. Transfer students are those that desire to transfer with or without a degree. General education refers to students who desire an associates degree without transfer as well as personal enrichment. Transitional students include those who want to improve basic skills and obtain their high school diploma (Institutional Research and Information, 2002). These figures represent the intentions of all students in the LACCD and may not provide the best picture of older student intentions. Older adult students may have varying backgrounds with special needs, concerns, and intentions. Settersten and Lovegreen (1998) identified various labels for older adult students. “Delayers” are those nontraditional students who have postponed their college experience but have the required qualifications to enter an educational institution. “Refreshers” include adults who are interested in updating their professional skills for career advancement. This group is becoming the fastest growing group of nontraditional students. Another group referred to as the “enrichers,” are those who have the primary intention of self-enrichment through their educational experience. A fourth group, “second-chancers,” are those students who may have chosen higher education for the first time and may not have the skills that are necessary to succeed. These students may require additional support services such as remedial courses and specialized counseling. The first three groups 35 may be more easily accommodated by higher education institutions as support systems for traditional students may be sufficient (Settersten & Lovegreen, 1998). In addition to the labels identified above, Eisen (1998) outlines a typology of older adult learning. In this description, Eisen uses four different categories of learning based on whether the learning is teacher or adult directed and whether the education is for credit or not-for-credit. The first category includes offerings that may be for credit and teacher directed. This may include typical college courses, professional programs or general education programs. The second category may include those programs that are for credit that are learner centered, such as distance education classes. Another category of programs are those that are learner centered and are not for credit. These types of activities may include the use of libraries, clubs, the Internet, or some types of support groups. Finally, those programs that are not for credit but teacher-directed, may include those programs that are offered by hospitals, senior centers and the like. These typologies of older adult learners go beyond simple age distinctions and are helpful to better identify the characteristics of special groups of students. Laanan (2000) conducted a study on degree aspirations that involved over 13,000 students enrolled in both private and public two-year colleges in the United States. Data was collected using a national sample of freshman students included in the Cooperative Institutional Research Program (CIRP). This study found that 50% of both private and public college students had degree aspirations of obtaining a 36 bachelors degree; almost 28% of private school students and 24.7% of public school students aspired to obtain a master’s degree; and 14% of private and 10% of public school students had aspirations of obtaining a doctorate. For all students, the same background characteristics were positively related to higher degree aspirations. These characteristics included: being a woman, and the educational level of both the father and mother of the student. Individual attributes such as high school grade point average and self-concept was also positively associated with degree aspirations. Further, younger students tended to have higher degree aspirations than older students. Unfortunately, this was a cross-sectional study, so there was no information available regarding actual degrees obtained (Laanan, 2000). In Voorhees and Zhou (2000), it was reported that 66.4% of students responding indicated that their goal in college was to obtain a certificate, degree, or transfer to a 4-year college. Improving job skills was reported by 21%, and 12% were attending for personal enrichment. Interestingly, over 79% of respondents stated that their original goals had not changed at the time of the survey from when they first enrolled. What was foimd was a significant, positive relationship between credits completed and shifts in intention. Although only 20% of students reported that their intention had shifted, these students had earned more credit hours than the other students overall. Therefore, the authors assert that the more prolonged the experience at a community college, the more likely students are to shift their goals, however they are also more likely to meet their goals. Further, younger students 37 indicated a higher level of perceived goal attainment, which may be due to shorter term goals of achievement differing from older students who tend to enroll on a part- time basis, leading to longer term goals (Voorhees and Zhou, 2000). Social support and older students. Both traditional and non-traditional students were compared on the basis of their social support networks and academic performance (Camey-Crompton & Tan, 2002). In this study, the authors defined traditional aged students as those who were between the ages of 18 and 22. The non traditional students were 35-44 years old. Sixty three women participated and information was collected regarding anxiety, depression, social support, and grade point average. Results of this study indicated that depression and anxiety scores were similar for both the traditional and nontraditional groups. Nontraditional students performed better overall academically than the traditional students. Further, the nontraditional students had more consistently high grades. Traditional students had larger numbers of individuals that provided them with social support than the nontraditional group, however there were no significant differences between groups in satisfaction with their social support. Psychological functioning of the nontraditional students did not vary with the amount of social support they received. However, when traditional students were less satisfied with their support network, they had poorer psychological functioning. Sources of support for younger participants more often included a boyfriend, grandparent, or parent. For the older students, a spouse, child, and other sources were more often reported. Further, it was 38 found that these older students experienced more self-reported stress including family commitments (Camey-Crompton & Tan, 2002). Barriers for older students. Jacobs (2002) conducted a secondary data analysis of the 1995 National Survey o f Family Growth and examined the characteristics and degree completion of women aged 15-44. Students that were under age 25, were more likely to be full time students and less likely to be married, have a young child at home and to be working full-time. The author found that while older age and previous attainment of a degree decreased the likelihood of current degree completion, when controlled for full or part-time enrollment, the data showed that working part-time was more predictive of degree completion. Thus, having more time to devote to an educational program, may determine whether a student persists regardless of their age or life situation. Another study that examined the relationship between barriers and achievement was conducted by Pascarella (2001) in which over 50 published articles were reviewed and summarized some of the findings from the National Study of Student Learning. This study was a three-year longitudinal study to examine the effect of both academic and nonacademic experiences on college student learning. This study used data that was collected on students from 23 colleges and universities across the country. Pascarella found that some part time work resulted in better academic achievement, but work over 15 hours (on campus) or 20 hours (off 39 campus) had a negative influence on learning. Age was not found to have as large an impact on this effect as the student’s work situation. Stress itself may also serve as an indirect barrier to some students succeeding. Dill and Henley (1998) examined the role of stress in traditional aged (18-23 years) and nontraditional aged (24-54 years) students. The participants reported the daily and major stressors occurred in a 3 month period. Participants reported stressful events by selecting them off of a list of 210 items as well as writing in responses. These events included items that were stressful, yet may have been negative, neutral or positive in nature. Items on this questionnaire included worrying about school, physical problems, or friends, for example. Students also rated each of these events in terms of frequency, desirability and impact on them. The result of this study showed that the traditional students reported attending class more often and worrying more about academic performance than the nontraditional students. The nontraditional students reported greater enjoyment doing homework but were also more affected by negative experiences in class or with instructors. Peer and social activities affected the traditional group more than the nontraditional group, however the nontraditional group reported having more home obligations (Dill and Henley, 1998). Home (1998) conducted a study o f nontraditional students who were 447 female students who had jobs and families and were over 23 years of age. Measures of life situations, institutional supports, perceived demands, and social support 40 networks were identified and examined to determine if they predicted role conflict, overload, and contagion. Life situations included a variety of demographic variables, student status, and family factors. Institutional supports included those from the work and/or educational institution. These supports included items such as distance learning, child care, course flexibility, workshops, and educational reimbursement. Perceived demands in this study referred to those arising from the participant’s job or schedule. Role conflict was defined as the perceptions of being pulled in different directions by different obligations. Overload referred to the sense of having to do too much. Role contagion referred to the degree to which participants felt preoccupied with one role while engaged in another. Results of this study indicated that lower income increased the participant’s vulnerability to role conflict. Higher perceived levels of student demands predicted role conflict, overload, and contagion. When students utilized distance education, they showed lower levels of conflict and contagion (Home, 1998). This study demonstrates that flexibility of programs and more creative institutional supports may be helpful to nontraditional students. Institutional strategies. Recognizing that perceived barriers and stress for older students may impact academic success in unique way .s important for institutions to attempt to better accommodate these students, riagedom, Perrakis, and Maxwell (2002) discussed ways to improve student retention in community colleges. The authors discuss ten “Positive Commandments,” which identify ways 41 that community colleges can help students to succeed. These commandments include providing opportunities for increased faculty-student interactions; more financial aid and affordable education; more flexible course offerings and class times; more 4-year college transfer centers; increased numbers of expert faculty; on campus assistance in learning and study centers; additional electronic resources for students; local campuses; more work study programs; and career counseling. This literature review supports several of these commandments that may be of particular importance to older adults students enrolled in community colleges. We know that older students may appreciate and benefit from increased faculty-student interactions and that older students may be more likely to challenge their instructors which would require more expert teachers (Wlodkowski & Kasworm, 2003). Therefore, creating opportunities for students to interact more with well informed instructors would be advantageous. Further, due to potential perceived barriers of work and family responsibilities it is imperative that colleges offer more flexibility in the choices that students have regarding the means to complete a course and the times that a course may be offered. It may also be helpful for older students to receive additional opportunities for tutoring or counseling, appropriate for their situation. Some community colleges have instituted learning assistance centers (LAC) to meet the needs of students, faculty, and staff as well as to improve student retention. These LAC’s focus on academic assessments, helping students develop 42 study skills, tutoring, technological assistance, staff development, referral, counseling and evaluation (Stem, 2001). Other names for LAC’s include “Academic Support Center,” “Academic Assistance Center,” or “Learning Support System,” and there are even internet sites that help to organize the resources and information regarding LAC’s across the country. Some LAC’s can be accessed electronically and others even provide services over the internet (Learning Support Centers in Higher Education website, 2003). Stem (2001) asserts that these centers may be of particular importance to nontraditional students or to those who feel out of place in college. The author further suggests that a learning assistance center may provide students with a connection that they may not otherwise find and help build the student’s sense of belonging and support their persistence in college. Cini and Fritz (1996) studied the effectiveness of a Saturday College program for nontraditional students. Two hundred and four nontraditional aged students (mean age o f 37) and 216 traditional age students (mean age of 19.5), were surveyed regarding the rewards and costs of attending college, the alternatives they had to college, and their level of commitment. The traditional students were enrolled in the regular college courses during the week. The nontraditional aged students were enrolled only in the Saturday College program to obtain bachelor’s degrees, For the traditional students, it was found that greater levels of commitment were predicted by perceived rewards for attending college. Commitment for younger students referred to both attachment to one’s college and the intention to graduate. Older 43 students also showed higher levels of commitment when they perceived that there were rewards associated with attending college. Older students also showed greater attachment to the college, but did not necessarily have the intention to graduate. For nontraditional age students, commitment was also predicted by the perception that they had few alternatives to attending college (Cini and Fritz, 1996). Another institutional strategy to accommodate older students, was the Project Assuring Student Success (PASS) program at Mercy College in Ohio. The PASS program was designed to address the issue o f low student retention rates. Mercy College, a two-year, private college, has similar concerns to that of other higher education institutions with dropout rates of up to 50%, changing demographics of students, and students requiring remedial education (Harter & Szurminski, 2001). The objective o f the PASS program was to provide support services to students and to develop a model of retention that reflects a diverse student body. As part of the PASS program, all students were required to attend an orientation program and take a “success strategies” class. Other services provided to students included: mentoring, progress reports, and advisement. Retention rates increased from 82% to 89.7% following the implementation of this program (Harter & Szurminski, 2001). There is much that colleges may do to provide an improved environment to facilitate greater success for older students. As indicated above, more flexible learning opportunities may be very beneficial for older students. However other more basic institutional concerns must also be addressed. Sissel, Hansman, & 44 Kasworm (2001) suggest that most colleges have not redefined themselves as serving adult students and they do not see their students as older with families or working. They further assert that college catalogs, web pages, admissions information, on- campus newspapers, and administrative office hours are all geared toward the younger more traditional student. Another institutional consideration may be to determine more appropriate measures of retention. While retention is typically measured longitudinally, a more clear perspective on older adult retention may be one that is cross-sectional in which student satisfaction and/or short-term objectives are measured (Kerka, 1989). Conclusions Nontraditional aged students are self-driven and pragmatic regarding their higher education experience. They are often responsible, self-supporting and have families. This differs from traditional aged college students who may still be preparing for adulthood. Student development strategies for nontraditional students may require a different model of persistence that may include specific types of counseling interventions as well as student support programs. Further, the perceived usefulness of the educational program may also determine whether an older, nontraditional student persists (MacKinnon-Slaney, 1994; Petersen & delMas, 1996). When a person experiences a new situation, that individual is required to evaluate the situation. The individual also assesses their own capacity to handle the situation, compares their skills with others, and determines their level of personal 45 efficacy. Any discrepancy between what the situation requires and what the individual believes that they may handle, affects self-efficacy (Bandura, 1977; Rebok & Offermann, 1983). Older adult learners returning to school may directly experience this threat to self-efficacy. Rebok and Offerman (1983) described the sources of efficacy information that may directly influence academic self-efficacy expectations for older students. Sources of efficacy information may include; past and present performance accomplishments such as prior college attendance and job skills; vicarious experiences such as mentors, colleagues, or media examples; verbal persuasion which would include encouragement or discouragement by family, counselors, peers, etc.; and physiological arousal such as excitement or anxiety regarding school. These sources of information affect self-efficacy expectations which in turn influence behavioral performance cognitively (classroom behavior), socially (interacting with students and instructors), and physically (energy). Perhaps helping older students to succeed in college includes fostering opportunities to provide positive sources of efficacy information (Rebok & Offermann, 1983). The research has shown that self-efficacy can positively impact academic performance in younger students and that there are ways to improve self-efficacy in these students. (Byer, 2001; Jackson, 2002; Lee & Klein, 2002; Luzzo et al., 1999; Perry et al., 2001; Spizer, 2000). It has also been shown that the level o f academic self-efficacy, self-regulation, and intrinsic motivation may play a role in student success and persistence (Eppler & Harju, 1997; Laanan, 2000; Voorhees & Zhou, 46 2000). The current study proposed will attempt to clarify the differences, if any, in academic self-efficacy, self-regulation, and motivation between younger and older students. This study will also attempt to determine the effect that these variables in addition to educational intentions have on course completion for older students. Implications Kussrow (2000) asserts that a new model for lifelong learning must be adopted especially for the community college. The author proposes that there is a need for modifying existing infrastructures and models that will more appropriately meet the needs of lifelong learners. Stages of learning and chronological segmentation would need to be dismissed in exchange for a system that stresses understanding the needs of the lifelong learner, better resource utilization, and building community partnerships. Additionally, it appears that some of the differences between traditional and nontraditional aged students may not be due to age at all, but perhaps varying roles and levels of responsibility for these groups. With this in mind, support programs for older students may be worthwhile, but differences in academic performance may not exist among varying age groups if lifestyles and goals are similar. It is also important to note that higher levels of academic self-efficacy, self regulation, and intrinsic motivation may help to improve student success. These variables may be improved through various interventions. Additional research 47 should focus on the nature of these concepts, especially for nontraditional aged students and how they may improve persistence and subsequent success. Persistence for older adult students may differ from that of traditional students. It may be misleading to consider older students “dropouts,” because they do not complete a program of study in a specified period of time. Nontraditional students may have various reasons for leaving (and returning to) a program, such as family issues or job requirements. Enrollment may also change due to changing personal goals, or because their goals have been met, without completing the program. In fact, students who “stop out,” that is, they attend, withdraw, and return, one or more times, is not atypical of nontraditional students and it may be misleading to consider them as dropouts or non-persisters (Kerka, 1995). Voorhees & Zhou (2000) suggest that the typical outcome measures used to determine the success of higher education institutions, may not be appropriate authors suggest that better understanding students intentions, shifts, and the factors that cause these shifts need to be considered in future research. 48 Chapter 3 Research Methodology Introduction This study was conducted utilizing a secondary data analysis o f the TRUCCS (Transfer and Retention of Urban Community College Students) data set. The TRUCCS data consisted of survey data collected from students and faculty in the LACCD, as well as transcript records of 5,000 students. The purpose o f this study was to determine how the levels of self-efficacy, self-regulation, and intrinsic motivation vary for students of various age groups and what effect these variables have on course completion for older students. Further analysis was conducted regarding how job related educational intentions affect course completion as well. Research Questions The following research questions were addressed in this study: 1. How do the levels of academic self-efficacy, self-regulation, and intrinsic motivation differ among students of various age groups? 2. How is course completion affected by academic self-efficacy, self regulation, and intrinsic motivation for students 40 years of age and older? 49 3. What types of educational intentions predict course completion for students 40 years of age and older? Hypotheses The previous research questions generated the following hypotheses: 1. The level of academic self-efficacy, self-regulation, and intrinsic motivation of students 40 years of age and above will be higher than students under 24 years of age. 2. Academic self-efficacy, self-regulation, and intrinsic motivation will predict course completion for students 40 years of age and older. 3. Educational intentions related to enhancing job skills will predict course completion for students 40 years of age and older. Methodology Research Population It is anticipated that this study will be generalizable to students within the LACCD and perhaps to other to urban community college districts with similar population demographics. Data from spring, 2003 showed that the LACCD educated 123,607 students The LACCD is also very diverse with the breakdown of students by ethnic backgrounds was: 14% Asian, 16% Black, 43% Hispanic, and 19% White. Female students made up 60% of all students. Most students (75%) held a high school diploma, 11% had a college degree of some type, and 13% had not graduated 50 high school. Twenty four percent of students were enrolled full time. Seventy-six percent of students were enrolled part-time, with 39% enrolled in fewer than six units. (Institutional Research and Information, 2002). More than one-forth of the students that are educated by the LACCD are over the age of 35 , and this enrollment is expected to continue (LACCD, 2003). Research Design This study was conducted as a secondary data analysis of TRUCCS data set. The 5000 students that participated in the initial TRUCCS study, was the sample used in this research. These students completed a questionnaire in the spring of 2001 and were from 241 classrooms across 9 campuses of the LACCD. Stratified random sampling was utilized to identify the participating classrooms which included three levels of English courses (two below and one at the transfer level) as well as samples of additional students in occupational programs. Follow up questionnaires were used to collect data in the second and third years. Students had the option of completing follow up surveys in hardcopy and online. Information from focus group interviews at the LACCD were also collected from administrators, faculty and students in the second year. In the third year, telephone surveys o f those students who were no longer enrolled at the LACCD were made to collect additional information on the students. Transcript data were also obtained for students who consented at the time of completing the first questionnaire. 51 Ninety-six percent of the sample consented. This dissertation used the information gathered from the initial questionnaire and transcript data only. Instrumentation The original TRUCCS study questionnaire was a 47-item instrument that addressed basic student demographics as well as items that assessed issues related to their thoughts and feelings about their college experience including: goals, roles, educational background, and family aspects. This questionnaire was piloted and then administered in the spring of 2001. Several studies have utilized the TRUCCS data to study community college students and course completion (Hagedom, 2005; Hagedorn, 2004; Hagedom & Cepeda, 2004) as well as the coursetaking patterns of students (Maxwell, et al, 2003). Data Analysis This study included both descriptive and inferential statistics utilizing the SPSS (Statistical Package for the Social Sciences) software, to examine the relationships among the variables in question. Descriptive statistics included frequencies, means, and cross tabulations to provide a description of students and their demographics. Variables examined were: age, gender, ethnicity, marital status, number of children, work status, degree attained, degree desired, degree level difference, parental socioeconomic status, student housing status (rent or own), cumulative grade point average, course completion rates, the number of registrations through the Spring of 2003, and reasons for attending college (intentions). Chi- 52 square tests were also run when appropriate to determine whether significant differences existed between groups An exploratory factor analysis was conducted in which thirteen items from the TRUCCS survey were reduced to three factors. These thirteen items were selected based on their relevance to the concepts of self-efficacy, self regulation and intrinsic motivation (Bandura, 1977; Pintrich & DeGroot, 1990; Zimmerman, 2002). A Varimax rotation with Kaiser normalization was used to determine factor loadings on each of the three factors and confirmed item selection for the following scales: self-efficacy, self regulation, and intrinsic motivation. Reliability analyses were conducted for each of the three scales. A correlation matrix was developed so that relationships between variables could be assessed. Correlations between all study variables were also analyzed to test for multicollinearity and to determine whether some variables, if any, required removal from the study. A multiple analysis of variance was conducted to examine the first research question. The dependent variables were self-efficacy, self regulation, and intrinsic motivation, while the independent variable was age group (young, middle, and older). A multiple analysis of covariance was also run to examine relationships between the dependent variables and student demographics. Transcript data was utilized to determine course completion rates for students. This variable was calculated as the ratio of the number of courses passed to 53 the number o f courses attempted. Grade point averages and the number of courses students registered in were also obtained from transcript data. An analysis of variance was conducted to determine whether significant differences existed between the three age groups and course completion means. Interaction effects caused by age were examined by developing cross- products of age and all key variables. These cross-products were then entered into a regression analysis to determine if any interaction effects due to age were present. Independent variables in the second research question were academic self- efficacy, self-regulation, and intrinsic motivation. The dependent variable was course completion. Multiple regression analysis was conducted with independent variables entered in blocks. Gender, ethnicity, and parental socioeconomic status were included in the first block. In the second block, marital status, number of children, work status, homeownership, and the number of course registrath ns were included. The third block consisted of the three independent variables. To examine the third research question, a multiple regression analysis was also conducted. In this analysis, the independent variables were three questions related to job related educational intentions. Participants were asked to rank their reasons for attending their college. Three work related reasons included in this analysis were: “I want to get a better job;” “To learn English for work;” and “This college offers the program or certificate I need for work.” The dependent variable was course completion. The first block consisted of the following variables: gender, 54 ethnicity, and parental socioeconomic status. The second block included marital status, number of children, work status, degree level, and homeownership. The third block consisted of the three independent variables. 55 CHAPTER 4 Results Introduction In this section, a description of the sample is provided, by reviewing participant demographics that were self-reported on the TRUCCS survey. The demographics are broken down by age group, to further describe the sample. An exploratory factor analysis identified three factors for which scales were developed to measure self-efficacy, self regulation, and intrinsic motivation. The age groups in this study (younger, midage, and older) were compared, utilizing a multiple analysis of variance to determine whether there were differences in self-efficacy, self regulation and intrinsic motivation. Further, multiple regression analyses were also conducted to identify whether self-efficacy, self regulation, intrinsic motivation, and job related educational intentions were predictors of course completion for older adult students. Description of the Sample In the TRUCCS sample, 57.5% of students were under 25 years of age (younger); 30.7% were aged 25-39 (midage), and 11.8% were 40 years of age and above (older). Of the sample, 39.1% were male, and 60.9% were female. The breakdown o f age and gender is displayed in Table 1. 56 Table 1 . Gender and Age Groups Age Group Younger Midage Older Total Male 1187 43.0% 522 35.3% 166 29.9% 1875 39.1% Female 1573 57.0% 957 64.7% 390 70.1% 2920 60.9% Total 2760 100.0% M79 100.0% 556 100.0% 4795 100.0% Chi-Square Value_______ df .745 2 There were no significant differences in ethnic groups within age groups. The overall breakdown of ethnic groups within the sample, as displayed in Table 2 were 12.0% Asian, 14.2% African-American, 49.0% Hispanic, 11.7% white, and 12.9% other ethnicities. 57 Table 2. Ethnicities and Age Groups_________________________________________ _________________________Age Group____________________ _____________ Younger________ Midage_________ Older_________ Total Asian 325 12.2% 173 12.3% 56 10.1% 554 12.0% African- American 368 13.9% 204 14.5% 84 15.2% 656 14.2% Hispanic 1288 48.5% 697 49.4% 279 50.5% 2264 49.0% White 314 11.8% 163 11.6% 63 11.4% 540 11.7% Other 356 13.4% 172 12.2% 71 12.9% 599 12.9% Refiised to state 6 .2% 1 .1% 0 - 7 .2% Total 2657 100% 1410 100% 553 100% 4620 100% Chi-Square Value_______ df 12.82 16 The younger group was largely unmarried, as shown in Table 3, with 95.8% unmarried as compared to the midage group (63%) and the older group (50%). 58 Table 3. Marital Status and Age Groups Age Group Younger Midage Older Total Not Married 2669 95.8% 940 63.0% 284 50.0% 3893 80.3% Married 117 4.2% 552 37.0% 284 50.0% 953 19.7% Total 2786 100% 1492 100% 568 100% 4846 100% Chi-Square Value_______ df 1036.34* 2 *p< 001 Table 4 illustrates that many students also had children living in their household, with 19.8% of the younger students, 49.3% of midage students, and 60% of older students having one or more children in their household. When asked about whether family responsibilities were a problem for them, 76.1% of younger, 61.8% of midage, and 70.0% of older students reported that family responsibilities were only a small or no problem at all. 59 Table 4. Number of Children in Household and Age Groups ________________________ Age Group Younger________Midage________ Older_________ Total None 2013 80.2% 730 50.6% 224 40.0% 2967 65.8% 1-2 354 14.1% 524 36.3% 243 43.4% 1121 24.8% 3-4 103 4.1% 169 11.7%% 83 14.8% 355 7.9% 5 or more 40 1.6% 19 1.3% 10 1.8% 69 1.5% Total 2510 100.0% 1442 100.0% 560 100.0% 4512 100.0% Chi-Square Value_______ df 564.65* 6 *p<001 Table 5 outlines the differences between age groups and work status. Younger students had the highest rates of working part-time: 45.9% (younger), 26.8% (midage), and 26.4% (older). However, the midage group had the highest rates of working full-time of 50.3%, with 22% of younger students and 42.9% of older students working full-time. 60 Table 5 Employment Status and Age Groups Age Group Younger Midage Older Total Not employed/ Not looking 335 12.1% 162 11.0% 85 15.4% 582 12.2% Not employed/ Looking 549 19.9% 177 12.0% 84 15.2% 810 16.9% Employed Part Time 1265 45.9% 395 26.8% 146 26,4% 1806 37.7% Employed Full-Time 609 22.1% 742 50.3% 237 42.9% 1588 33.2% Total 2758 100.0% 1476 100.0% 552 100.0% 4786 100.0% Chi-Square Value_______ df 390.00* 4 *p<001 Students that reported that job responsibilities were a small or not a problem at all were: 64.5% (younger), 57.4% (midage), and 66.7%. Other students thought that their job responsibilities were a medium, large, or a very large problem. This included 35.5% of younger students, 42.6% of midage students, and 33.2% of older students. Pearson chi-square testing with symmetric measures indicated that there 61 was a significant difference between these variables; Chi-square (p< 01) and Kendall’s tau-b (p< 01). Students also indicated whether they were interested in advancing their education for job related reasons. Over 81% of younger students were interested in improving skills for a job, as compared with 87.6% of midage students, and 79.4% o f older students. Chi-square and Kendall’s tau-b tests indicated that there were significant (p<01, p<05, respectively) differences between the variables of age group and desire for job skill enhancement. Those students who had already earned an associates, bachelors, or graduate degree included 8.5% of younger students, 23.4% of midage students, and 34.0% of older students (see Table 6). Chi-square tests indicated that there were significant differences between age group and degree status at the p<01 significance level. 62 Table 6 Degree Earned and Age Group Age Group Younger Midage Older Total No degree/ Certificate 2564 91.5% 1145 76.6% 378 66.0% 4087 83.9% Associate 141 5.0% 199 13.3% 101 17.6% 441 9.1% Bachelor's 25 .9% 102 6.8% 59 10.3% 186 3.8% Graduate 73 2.6% 49 3.3% 35 6.1% 157 3.2% Total 2803 100.0% 1495 100.0 % 573 100.0% 4871 100.0% Chi-Square Value df 356.52* *p<001 6 63 Table 7 shows more specifically, the degrees desired by age group of students. Differences between age group and degree intentions were significant at the p< 01 level. Overall, most students had degree goals. Only .7% of younger, 1.4% of midage students, and 5.2% of older students reported that they had no degree goal. Percentages of students that had the intention of obtaining a bachelor’s degree were 31.7% (younger), 35.7% (midage), and 36.0% (older). On the other hand, some students had higher degree aspirations. Over 61% of younger, 52.0% of midage, and 39.4% of older students had graduate degrees as their goal. Table 8 identifies the difference between the level of degree desired and the level of degree attained already. Over 80% of all students wanted to obtain two or more additional levels of schooling. For instance, a student with an associates degree may desire a masters degree or doctorate. Or, a student with no degree, may aspire to a bachelors degree or beyond. 64 Table 7 Highest Academic Degree Desired and Age Groups Age Group Younger Midage Older Total No degree 19 .7% 20 1.4% 29 5.2% 68 1.4% Vocational Certificate 26 .9% 37 2.5% 30 5.4% 93 1.9% Associate 153 5.5% 124 8.4% 78 14.0% 355 7.4% Bachelor's 231 8.4% 194 13.1% 93 16.7% 518 10.8% Maybe More than Bachelor’s 646 23.4% 334 22.6% 107 19.2% 1087 22.6% Master's 83 S 30.3% 428 28.9% 99 17.8% 1365 28.4% Doctoral 551 19.9% 258 17.4% 103 18.5% 912 19.0% Medical 301 10.9% 84 5.7% 17 3.1% 402 8.4% Total 2765 100.0% 1479 100.0% 556 100.0% 4800 100.0% Chi-Square Value________df 211.31* *p<001 6 65 Table 8 Degree Difference (Desired-Earned) and Age Group Difference Age Group Younger Midage Older Total -3 5 .2% 3 .2% 1 .2% 9 .2% -2 12 .4% 12 .8% 7 1.3% 31 .6% -1 26 .9% 26 1.8% 21 3.8% 73 1.5% 0 96 3.5% 105 7.1% 103 18.5% 304 6.3% 1 173 6.3% 227 15.3% 122 21.9% 522 10.9% 2 897 32.4% 542 36.6% 171 30.8% 1610 33.5% 3 1556 56.3% 564 38.1% 131 23.6% 2251 46.9% Total 2765 100.0% 1479 100.0% 556 100.0% 4800 100.0% Chi-Square Value________df 489.94* *p<001 12 6 6 An analysis of variance indicated that there were no differences between age groups for parental socioeconomic status. Parental socioeconomic status was determined by taking the sum of the mother and/or father’s occupational status score (Terrie & Nam, 1994). The occupational status score is a value between 0 and 100, and takes into consideration the education and income associated with specific job titles. More specifically, the value given as an occupational status score indicates the percentage o f individuals in the entire workforce that have less combined income and education than the person in that occupation. The minimum parental occupational status score (socioeconomic status) was .70, the maximum was 199.60, and the mean was 75.60. Student homeownership status was also included into the two regression analyses. Students reported whether they owned or rented their home. Only 8.2% of younger students owned their homes, while 21.5% of midage students, and 37.7% of older students were homeowners. Chi-Square tests indicated that these differences were significant at the p<.001 level. The mean course completion rate for the entire sample was .687. For younger, midage, and older students, mean course completion rates were: .682, .691, and .699, respectively. Cumulative grade point averages also increased slightly by age group. On a four point scale, mean grade point averages for younger students was 2.49; for midage students, 2.52; and for older students 2.54. The numbers of registrations in the LACCD by all students averaged 34.83 registrations. Younger 67 students averaged 34.30 cumulative registrations, midage students averaged 35.77, and older students averaged 34.97 registrations over the course of their time at the LACCD. No significant differences between groups were found between the three age groups and course completion means, cumulative grade point average, and the number of course registrations. The correlation matrix, Table 9, identifies a pair o f variables, course completion and cumulative grade point average, with a high Pearson correlation of .820. Due to this high correlation, cumulative grade point averages were not utilized in the multivariate analysis. Table 9. Correlation Matrix of Key Variables *p< 0.05 (2-tailed); **p<0.001 (2-tailed) 8 9 10 11 12 13 14 15 16 17 38 39 20 21 Mean SD 1. Age 1.000.089** .005 ,171**-.170** .089** -.015 -.004 .020 -.004 .041* .194** .114** .161** -.004 -.011 .004 .010 -.034* .001 -.013 26.828 9.459 2. Gender .089** 1.000 -.013 .064** -.002 -.044** -.010 -.017 .009 .003 .024 .106** .086** .088** .023 .002 .004 .001 -.001 -.004 -.005 1.605 .489 3. Asian .005 -.013 1.000 -,130**-.297**-.I16**-.123** .018 -.012 .033* .027 .102** .096** .001 -.031* -.011 -.012 .022 -.009 .019 .015 .106 .308 4. Black .171** .064**-. 130** 1.000 -.326**-. I28**-.135** -.019 -.022 -.008 -.010 -.083**-.Q91** .044* .018 -.009 .021 .012 -.001 -.030* -.024 .125 .331 5. Hispanic -.170** -.002 -.297**-.326** 1.000 -,292**-.309** .031* .025 -.032* .011 -.132**-.070** .018 .047** -.017 .002 .012 .006 -.024 .013 .427 495 6. White .089** -.044*-. 116**-. 128**-.292** 1.000 -.121** -.033* .015 -.011 .007 .143** .078** -.069** -.004 .016 -.008 -.027 -.012 .004 .022 .103 .303 7. Other -.015 -.010 -. 123**-. I35**-.309**-.l 21 ** 1.000 -.022 -.031* .00 6 -.010 .047* .032* -.008 .000 .021 .009 -.017 .011 -.015 -.036* .114 .317 Ethnicity 8. Marital -.004 -.017 .018 -.019 .031 -.033 -.022 1.000 .340 .111 -.023 .002 .014 -.008 -.075 .026 .062 .313** .064** .133** .132** 1.200 .400 Status 9. Number .020 .009 -.012 -.022 .025 .015 -.031* .340** 1.000 .040* -.057** .020 .017 .016 -.048* .045* .027 .160** .081** .087** .143** 1.450 .710 Children 10. Work -.004 .003 .033* -.008 -.032* -.011 .006 .111** .040* 1.000 -.019 -.004 -.007 -.005 .057** -.073** 052** .135** .048** -.040**-.039** 1.263 .680 Status 11. Parental .041* .024 .027 -.010 .011 .007 -.010 -.023 -.057** -.019 1.000 .028 .031 .017 .176** -.006 -.011 .026 -.088** -.119** -.107** 1.198 1.490 SES 12. GPA .194** .106** .102**-.083**-.132*» .143** .047* .002 .020 -.004 .028 1.000 .820** .133** -.008 .022 .002 .017 -.007 .015 -.020 .687 .244 13. Course .114** .086** .096** -.091 **-.070** .078** .032* .014 .017 -.007 .031 .820** 1.000 .114** -.013 .017 .007 .005 -.020 .023 -.023 34.800 23.030 Completion 14. Number .161** .088** .001 .044* .018 -.069** -.008 -.008 .016 -.005 .017 .133** .114** 1.000 -.005 .005 -.013 .025 -.017 -.001 -.013 26.829 9.459 Registrations 15. Self -.004 .023 -.031* .018 .047* -.004 .000 -.075**-.048* .057** .176** -.008 -.013 -.005 1.000 .000 , 390**-.043**-.082**-.450**-.149** 3.307 .593 Efficacy 16. Self -.011 .002 -.011 -.009 -.017 .016 .021 .026 .045* -.073** -.006 .022 .017 .005 .000 1.000 .120** -.012 .036* .118** .132** 1.918 .835 Regulation 17. Intrinsic .004 .004 -.012 .021 .002 -.008 .009 .062** .027 .052** -.011 .002 .007 -.013 .190** .120** 1.000 .003 .095** -.011 .063** 6.287 .715 Motivation 18. Student .010 .001 .022 .012 .012 -.027 -.017 .313** .160** .135** .026 .017 .005 .025 -.043** -.012 .003 1.000 -.013 .053** .031* 1.160 .360 Home 19. Better -.034* -.001 -.009 -.001 .006 -.012 .011 .064** .081** .048**-.088** -.007 -.020 -.017 -.082** .036* .095** -.013 1.000 .240** .337** 5.700 1.970 Job 20. English .001 -.004 .019 -.030* -.024 .004 -.015 .133** .087**-.040**-.] 19** .015 .023 -.001 -.450** .118** -.011 .053** .240** 1.000.382** 3.390 2.460 for Work _ 21. Program -.013 -.005 .015 -.024 .013 .022 -.036 .132 .143**-.039**-.107** -.020 -.023 -.013 -.149** .132 .063** .031* .337** .382** 1.000 4.270 2.380 oo for Work 69 Multivariate Analysis In the factor analysis, eigenvalues were 4.447, 2.782, and 1.843 for each of three factors, self-efficacy, self regulation, and intrinsic motivation. These three variables accounted for 56.70% of the total variance. Factor loadings are listed in Table 10. Reliability analyses were conducted for each of the three scales. The scales of self-efficacy, self regulation, and intrinsic motivation had alphas of .8959, .7367, and .7063, respectively. The means for self-efficacy, self regulation, and intrinsic motivation for all age groups are identified in Table 11. A multiple analysis of variance revealed a significant difference between the means of the dependent variables: self-efficacy, self regulation, and intrinsic motivation for the three age groups (Wilks’ Lambda=,976, F=19.493, df=6, 9732, pc.001). 70 Table 10. Items and alpha coefficients of scales. Scale Name Items Factor Loadings Possible Responses 19 How well are you able 1. Not at all to do the following in 2. With difficulty Academic English? 3. Fairly well Self Efficacy a. Understand a college .830 4. Very well Alpha = .8959 lecture b. Read a college text .849 book c. Write an essay exam .867 d. Write a tenn paper .843 13 Approximately in the 1. 0, or didn’t have past 7 days, did you: time a. Talk with and instructor .721 2. 1 time before or after a class 3. 2 times b. Talk with an instructor .695 4. 3 times during office hours 5.4 times c. Study in small groups .636 6 5 times or more Self Regulation outside of class Alpha = .7367 d. Speak with an .661 academic counselor 14 For this course only. approximately how many times in the past 7 days. did you: a. Telephone or email .563 another student to ask a question about your studies? b. Ask the instructor .660 questions? 37.6. Understanding what .741 1. Strongly agree is taught is important to 2. Disagree Intrinsic me... 3. Slightly disagree Motivation 37.8. I keep trying even .649 4. Not sure Alpha = .7063 when I am frustrated by a 5. Slightly agree task... 6. Agree 37.15. I feel most .769 7. Strongly agree satisfied when I work liard to achieve something... 37.17. Success I college .717 is largely due to effort... 71 Table 11. Statistics: Research Question #1 Dependent Variable Age Group Mean Standard Deviation Self-Efficacy Younger 3.3387 Midage 3.2671 Older 3.2568 .5764 .6200 9.362* .6194 Self Regulation Younger 1.8597 Midage 1.9355 Older 2.1174 .8010 .8501 23.758* .9071 Younger 6,2287 Intrinsic Motivation Midage 6,3673 .7600 .6587 22.506* Older 6.3693 .6258 *p<001 72 Table 12 displays results for age group comparisons of means for self- efficacy, self regulation and intrinsic motivation. Significant differences were found between age group and both self-efficacy and intrinsic motivation for younger and midage groups as well as for the younger and older groups (p<01). All groups showed significant differences for self regulation: younger and midage (p<05); younger and older (p< 01); and midage and older (p< 01). The midage and older age groups did not have significant differences between the means for neither self- efficacy nor intrinsic motivation. Table 12. Pairwise Comparison o f Means: Research Question #1______________ Dependent Variable Groups Mean Difference Standard Error Younger - Midage Self-Efficacy Younger - Older Midage - Older .0715* .0191 .0819* .0273 .0104 .0292 Younger - Midage Self Regulation Younger - Older Midage - Older -.0759** .0266 -.2578* .0380 -.1819* .0408 Younger - Midage Intrinsic Motivation Younger - Older Midage - Older -.1386* .0229 -.1405* .0328 -.0020 .0351 *p<01; **p<.05 73 Table 13 displays the results from a multiple analysis of covariance. The results indicate which variables significantly influence the three dependent variables: self-efficacy, self regulation, and intrinsic motivation. Parental SES, marital status, work status, and taking English for work, all have an effect on self-efficacy. Work status, student homeownership, age group, being Hispanic, desiring a better job, taking English for work, and access to a program needed for work, influenced self regulation. Finally, marital status, age group, desiring a better job, and access to a program needed for work, effected intrinsic motivation. 74 Table 13. Multiple Analysis of Covariance: Tests of Between-Subjects Effects Covariatcs Dependent Variable F Course Completion Self-Efficacy 1.019 Self Regulation .427 Intrinsic Motivation 2.680 Gender Self-Efficacy .880 Self Regulation .038 Intrinsic Motivation .036 Asian Self-Efficacy 3.348 SelfRegulation 3.558 Intrinsic Motivation 2.685 African-American Self-Efficacy 1.721 SelfRegulation 1.755 Intrinsic Motivation 2.379 Hispanic Self-Efficacy 1.276 SelfRegulation 4.296* Intrinsic Motivation .180 Parental SES Self-Efficacy 57.145** SelfRegulation 1.805 Intrinsic Motivation .352 Marital Status Self-Efficacy .176 SelfRegulation 1.583 Intrinsic Motivation 4.344* Number of CliildrenSclf-ElTicacy .570 SelfRegulation .005 Intrinsic Motivation .060 Work Status Self-Efficacy 6.654* SelfRegulation 16.324** Intrinsic Motivation 1.578 Number of Self-Efficacy .300 Registrations SelfRegulation .935 Intrinsic Motivation .773 Student Home Self-Efficacy .000 SelfRegulation 11.832* Intrinsic Motivation 2.548 Better Job Self-Efficacy .000 SelfRegulation 11.832* Intrinsic Motivation 2.548** English for Work Self-Efficacy 608.484** SelfRegulation 12.591** Intrinsic Motivation 2.212 Program for Work Self-Efficacy 3.273 SelfRegulation 21.065** Intrinsic Motivation 4.791* Age Group Self-Efficacy .044 SelfRegulation 10.106** Intrinsic Motivation 6.643* *p*..05; **p<.001 75 A significant interaction by age on study variables was found. Table 14 identifies the cross-products derived. These cross-products, when entered into a regression anr 1,,sis, demonstrated that there were significant interaction effects by age (F-change=8.352, dfl=8, df2=4660, pc.OOl). Consequently, the study sample was split by age for the multiple regression analysis, including only those that were 40 years of age and above in the regression analyses. To examine the second research question, a multiple regression analysis was conducted to determine the effect of self-efficacy, self regulation and intrinsic motivation on course completion. Table 15 lists the variables included in each of the tlree blocks in the order of their entry into the regression. Table 14. Cross Products to Test for Interaction Effects Item C rossed With Age Mean SD Gender 43.467 21.512 Asian 2.811 8.781 Black 3.832 10.901 Hispanic 10.730 13.436 White 2.967 9.563 Other 2.967 8.933 Marital Status 32.098 15.833 Number of Children 39.186 24.773 Work Status 54.505 29.536 Educational Intentions 91.753 39.602 Parental SES 2062,344 1538.224 Student Home 1.160 .360 Number Registrations 969.252 897.241 Better Job 152.382 76.67 English for Work 89.42 76.60 Program for Work 114.13 78.65 77 Table 15. Multiple Regression Block Entry*: Research Question #2_________________ Block R2 R2 Sig. Change F Change Gender .087 .087 .000 Asian Black Hispanic Parental SES Marital Status , m .024 .016 Children Work Status Number of Registrations Student Ilomeownership Self-Efficacy .124 .013 .058 Self Regulation Intrinsic Motivation *N=528 78 An analysis of variance indicated that each of the blocks explained a significant amount of variation (p< 001) in course completion (block 1, F=9.906; block 2, F=6.450; block 3, F=5.585). The first and second blocks significantly explained 8.7% and 11.1% of the variance, respectively, in course completion. Self- efficacy, self regulation, and intrinsic motivation in the third block did not significantly predict course completion in the regression analysis; Individual items: self regulation, parental socioeconomic status, and being Asian, were found to significantly (p<05) predict course completion as presented in Table 16. Being African-American, Hispanic, and owning a home negatively predicted course completion. 79 Table 16. Regression Coefficients*: Research Question #2__________________________ Standardized Independent Variables Unstandardized Regression Coefficients Coefficients B Standard Error Gender .041 .021 .082 Asian .071 .033 .100** Black -.113 .024 -260*** Hispanic -.052 .025 -.099** Parental SES .000 .000 .084** Marital Status .046 .028 .076 Number of Children .021 .014 .065 Work Status -.020 .013 -.067 Number of Registrations -.001 .000 -.070 Student Home -.062 .030 -.092** Self-Efficacy -.022 .017 -.056 SelfRegulation .025 .012 .090** Intrinsic Motivation -.014 .014 -.044 *N=528; **p< 05; ***p< 001 80 Table 17 describes the blocks that were included in the multiple regression analysis to answer the third research question examining the relationship between job related educational intentions and course completion. An analysis of variance indicated that each o f the blocks explained a significant amount of variation (p< 001) in course completion (block 1, F=9.906; block 2, F=7,l 15; block 3, F=5.922). The first two blocks entered into the regression significantly explained 12.1% of the variance combined in the dependent variable. The third block did not significantly Table 17. Multiple Regression Block Entry*: Research Question #3__________________ Block R2 R2 Change Sig. F Change 1 Gender Asian Black .087 .087 .000 Hispanic Parental SES 2 Marital Status Children Work Status Student Homeownership .121 .034 .001 3 Better Job English for Work Program for Work .130 .009 .141 *N=528 81 predict course completion. As shown in Table 18, predictors of course completion included: being Asian, having a degree, and taking English for work. There were also variables that predicted course completion in a negative direction. These variables were being African-American or Hispanic, working more hours, and being a homeowner. Table 18. Regression Coefficients*: Research Question #3 Independent Variables Unstandardized Standardized Coefficients Regression Coefficients Standard B Error Gender .040 .021 .082 Asian .082 .032 .115** Black -.127 .025 -.248*** Hispanic -.051 .025 -.098** Parental SES .000 .000 .062 Marital Status .036 .029 .059 Number of Children .019 .015 .059 Work Status -.026 .013 -.086** Student Homeownership -.067 .030 -.100** Degree Attained .056 .022 .109** Better Job -.001 .023 -.001 English for Work .052 .022 .101** Program for Work -.017 .022 -.035 *N=528; **p<.05; ***p<001 82 Summary The results found support some o f the study hypotheses, but not all. It was hypothesized that older students would have higher levels of academic self-efficacy, self-regulation, and intrinsic motivation than students in the younger age group. Differences in the means indicated that this was the case for both self regulation and intrinsic motivation. However, this was not the case for self-efficacy. In fact, older students had significantly lower means for self-efficacy than both the younger and midage groups. The second hypothesis asserted that academic self-efficacy, self-regulation, and intrinsic motivation would be positively related to course completion for students 40 years of age and older. While self regulation was found to significantly influence course completion for older students, neither self-efficacy, nor intrinsic motivation were predictors of course completion. Being Asian and parental socioeconomic status, were also predictors of course completion. Negative predictors included being African-American, Hispanic, and owning a home Finally, the third hypothesis stated that educational intentions related to job skill enhancement, would have the greatest rate o f course completion for students 40 years of age and older. Taking English for work was a predictor of course completion, however the two other job related variables were not. Having a degree, and being Asian, were also predictors of course completion. Additional variables 83 were found to negatively predict course completion. These variables were being African-American or Hispanic, owning a home, and working more hours. 84 CHAPTER 5 Conclusions Introduction This study was an attempt to examine whether self-efficacy, self regulation, and intrinsic motivation was different for older students than for younger students. An examination was also conducted of how these variables along with job related educational intentions, effect course completion,. While not all of the research hypotheses were confirmed, aspects of this study may provide a better understanding of the higher education experience for older adult students. This section will review the study findings, implications, and opportunities for future research. Findings Self-efficacy levels were lower for older adults, indicating that academic confidence may be lower for older students. Older students however, had higher levels of self regulation than both the midage and younger students in this study. Self regulation also predicted older student success. Possessing self regulation, may be helpful to ensure success for the older student. The lower level of self-efficacy in these students may have been compensated by higher levels of self regulation (Zimmerman, 2002). Degree level was also a predictor o f course completion, which may be an indication that students with prior college experience are more “skilled” at successfully completing a course, and/or may be more self regulated learners. 85 Needing to improve English skills may be an important motivator for some students over 40 and may drive them to have better course completion rates than their peers. However, the mere desire of a better job and being aware that job related programs were available to them, were not enough to improve course completion rates for older students. It has been shown that nontraditional students demonstrate greater persistence if they believe that their educational program will result in greater job opportunities (Peterson & delMas, 1996). Having the desire to take English for work may be motivating enough to generate greater student success. Goals that are more specific may actually help to improve student performance (Schunk, 2001). Perhaps the specificity o f improving English, versus wanting a better job or completing a certificate, is enough to create improved student persistence. It was also found that older students that worked fewer hours, had greater course completion rates. Working too many hours may be an obstacle for students to perform well academically. While this may simply be due to a student’s lack of time to devote to an educational program, it may also be an indication of the need to tailor more courses and programs for working adults. In both of the regression analyses, ethnicity and socioeconomic factors were predictors of course completion for students 40 and older. Parental socioeconomic status predicted course completion in the first regression. However, student homeownership negatively predicted course completion in both regression analyses. Three ethnic variables: Asian, African-American, and Hispanic were also predictors, 86 however the direction of the relationships differed, with the former being positive and the two latter, negative. Issues related to ethnicity and socioeconomic status are complex and may be clouded by other aspects of a student’s background. Implications and opportunities for future research are discussed in the following sections. Implications As this study revealed, self regulation positively predicts course completion for students 40 years o f age and older. Colleges that provide programs to help develop better self regulation skills in their students, may foster greater student success among older students. Practical interventions may include programs to promote better study strategies, time management, goal setting, note taking, and assertiveness training to ask for assistance when needed. Some job related educational intentions may help to improve course completion, however, working more hours may hinder course completion, based on the current study. It may be that students who want to improve some job skills do very well— as long as they remain in class. Those students who work more hours, may need some additional support to keep them in school. Support services and institutional adaptations such as flexible course times and classroom formats, greater ease in registering and obtaining student services, may all be important to consider for working students of any age. Providing assistance with the development o f self 87 regulating behaviors may also be of help in some areas, such as time management and goal setting. Parental socioeconomic status in one of the regression analyses, was a predictor of student success even though these students were 40 years of age and older. Home (1998) reported that lower incomes created added s ess for students, which may in turn, affect how students perform academically. Tnis study indicated that parental socioeconomic status may impact student performan ce well into adulthood, and not only while growing up. Owning a home, however, was a negative predictor of course completion. Perhaps those student;', who own homes, are faced with the added stress o f having a mortgage or othei family responsibilities, and are less likely to complete their courses. Or, they may be more financially secure, and not as driven to follow through on their education, as it may not be necessary to obtain a better job or improve their financial situation. Additional research is needed to understand the relationship between these variables. In addition to working more hours and owning a home, being African- American or Hispanic were negative predictors of course completion. In the LACCD overall, African-American students have the lowest persistence rate, with Asian students having the highest (Institutional Research and Information, 2002). The broad ethnic categories that were utilized, may have effected results. For example, the category of “Asian” includes many ethnic groups such as Japanese, Chinese, and Korean, among many others. Although differences in specific ethnic 88 groups may exist, this grouping was necessary due to sample size. This procedure is also consistent with current data collection procedures at the LACCD and elsewhere. Future research may include samples of specific ethnic groups and may shed light on the issue of ethnicity and course completion. These ethnic variables may also be confounded by other individual background variables and should be examined. Programs designed to assist at risk students should be important for community colleges to provide, regardless o f ethnicity, age, background, or work status. Future Studies The TRUCCS survey was not specifically designed to reach older adult students, but was instead designed to identify a representative sample of students at an urban community college. This sample was helpful to examine some relationships between age groups, however, future studies should place more emphasis on older students. Many older students are lifelong learners and may take courses for enrichment, to upgrade job skills, or to obtain a degree and change careers (Settersten & Lovegreen, 1998). In addition, nontraditional students face many obstacles, all of which need to be considered when designing research and data collection tools. Theories previously tested on younger students, should also be considered for older adult research. This study indicated that self regulation may be a predictor of course completion for older adults. It was also demonstrated that the means o f self regulation do increase with age, however other variables may play a role in this 89 relationship and should be examined. Self-efficacy and intrinsic motivation have also been shown to have a positive effect on academic goals and intentions of younger students (Pajares, 2002). These relationships should be explored specifically for older students to determine whether these results apply to them as well. The individual background factors of older students may affect success rates. These background factors along with socioeconomic status and ethnicity, require additional study. A closer and more detailed examination of a student’s prior history including academic performance, social support, family issues, extracurricular activities, etc., may shed greater light on what may increase student success. This added information may facilitate the development of student support programs that may better assist students in reaching their greatest potential. Issues related to possessing multiple roles, including that of student and employee, may be pertinent to the study of older students, and should be considered for future research, Mdst students over 40 years of age (and even more midage students) have families and also work full time. All students are faced with barriers and time constraints while in school; However, the older student may have a unique combination of issues and concerns. These issues should be further explored, to help determine how they may impact the success of older adults in higher education. Job related educational intentions of older students also require additional study. In the current research, the only job-related reason for attending school that 90 was a predictor o f course completion was taking English for work. While it has been suggested in the findings that more specific goals such as this one may improve student success, this relationship may be better examined in future research by comparing students who were native English speakers with those who were not, Future research should also examine older student intentions and goals over time. While so many institutional measures of success involve attaining degrees or maintaining enrollment over a period of time, older student success may not be so simple to measure. Statistics including dropout rates, retention, and intentions may be misleading when describing the performance of older adult students, as they may not take into account other goals of the student (Voorhees & Zhou, 2000; Kerka, 1995). New definitions of success and collecting data may be necessary to develop a clearer understanding of the older student’s experience. Older student goals may include graduating, however other measures of success may include whether he or she is satisfied with their educational experience and whether they have experienced any positive employment and/or life changes. Traditional persistence rates, grade point averages, and degree attainment may also be appropriate when multiple measures are taken over a longer period of time, as older students often change goals regularly (Summers, 2002). Course-taking patterns over time may also sned more light on the registration habits of older adults. The majority of students entering higher education institutions today are nontraditional. A large percentage of these nontraditional students are older as well. 91 Further research in the areas mentioned above may help to better understand how greater success may be facilitated in older students. 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Theory into practice, 41, 64-70. 100 Appendix The Transfer and Retention of Urban Community College Student (TRUCCS) Questionnaire 101 Community College Student Survey Dear Student: This iniormation is being collcaed by researchers from the University of Southern Calitorniii and the University of California at Los Angeles in conjunction with the Ian. Angeles {'(immunity College District as [tart oi a large study oi community college students in Los Angeles. You have been s e le c t e d as a participant in a multi-ye«r project. Your cooperation will nssisl researchers to help Los Angeles Community College students to ho successful tit their educational pursuits Your assistance tv crucial to the project: we thank you for your participation in this important research DIRECTIONS P lease answer all questions a s completely and accurately a s possible. Because your responses will be read b y a machine, your careful o&aervonc a of these few sim ple rules will tw m eat appreciated. » Us® only black lead pencil {No. 2 is Ideal). e v e u u i bo v Maks heavy black marks that ill) the ovals (do naidrtlo or check thft ovals) Correct Hark: Incorrect Mark: » Erase cleanly any answer you wish to change. C O * - 0 it {K @ iiP • Make no stray markings of any kind. Name: Ycur primary email address: Ycur phone number- . . . ___ Social Security Number TTTTTT ■ t. 3/ c '© •j, itt> r ©I I f T T . T ' X i r T - ^ T .i p i - x > i-'X i j v -i :a;j [* x c t » ta«t |.T « Ti Crt> f -i.cpj ‘ x r x i t 8 ci*'t x | i . 5 f, C - x s -tsv.i 3 ; fcr- k \Tj i t t rr* y. <jY k r » •« » « xn ®.i id br~T. J . t a t JQXvK We went to follow your progress for the next two years; yet we realize that many students will m ove from time to time. P lease provide the nam es of two people who are likely to know your address even if you move. We request the nam e, address, and phone number of two perso n s. Contact 1: A relative or friend who Coes not „ve with you ant) who is likely to know your address at ajj times: Name: ______ _______________________ ______________ A ddress:____ City, S tate Zip , Phone Number, Email address.« C ontpct 2: Another relative or friend who doss not live with you an d whg is likely to know your address at all times: Name: ___________ .______ ________ ___________ A d d ress:. City, S tato Zip1. Phone N um b ei Email ad d ress:. p o * | o | | o | l o t o n c n o o o o o o 1 1 6 9 9 DO NOT w a n t IN THIS AftEA 102 1, Below are som e reason s that mtgtil have Intlunncsd your decision to ettand th is particular colloga. How Im portantlw as e a c h roasott in your decisio n to c o m e here? tWah, rjns (or nach sratfH nanl) My pnronts v/anted mo 'o r.r.mr hnic M y c p u u s o |j-.r tn r-r o r o t h e r tsrn iry member wanted mo to corns *w« This college h as a good reputation 1 wanted to go to a tjjffgrgnt cofogo than many or my M e n d s ................... Th 9 catregn h as good aooia activities I conlijn I (Inn a yah . Th s coltego is o'ferdablo . . A nigh ser-acl or othar dpunf.nior S C M o sd n ts . .. Th-s eoltegtr w ctoss to m y ''tune Th s coIMqo 5 graduates r jp t gir.'J jnhs T he college s students transfer to good it-yonr Befools t coutdnl find anything hatter la cm I want to gat a batter pib . . . My Iriondo arc nliending h e r e ------ T hs co iag e > e owao to wnoro t w yk Tha cottas® offara oducefcmtal program s of special interact to mo lltet olhor GT/ogc.ii do NOT have i n an t t o get a cortege d e g -e e . To irrnrn Fnglsft tor wavs My ompteyer shccuraoed rns to ahio i here • • • . T he college oilers Ihe program or certificate I need tar w o r n ................. 2, How many of your closest personal friends are also currently attending this college? (Mark one ( None nf rry closest Inends ............... One oi my dosesl Mends A faw ot my cioecat teands ............. A& cul h a lt ct nry closest Moods .. Most of my iposesi Menas . , At ol my c-osfist fnsnds 3. In general, w hat d o th # following p e o p le think ab o u t th la p a rtic u la r collage? IMnrk e r a ter each statement.I You . Yaur cteaosl Irlcndrr . . Your spouse or partner . . . Your parents or guardians Yaur olher rmal'UBB . Yam high school ledftlvets O thers ........................ I l M r * wiki! i T. W hich of te a following stato m an ts b est doscrm os your CDttege p la n s to r next se m e ste r? (Mark o re. i f wit attend only this c d le g o . . . ■ I wfl astondrhic ooi'sgo and t ether college . .. I will attend this r.nitefl8 and 2 o ' note other c o lle g e s ..................- I wil( not utlund hnto, hu* I will attend t olhpr college I will nol attend new. M I will a lte r'd ? nr iron* u li. / ro le y e s " I will not niton:! any conogo. . ...................... S. W here did you attend sch o o l? D rilled Anothr (MuT, ah that apply *n m et t,;k.Trr i S tales Countr feiomarKaryricho3l nr namv.nlort (Agar, 4 to It) . .luulrjf hlgf bcIk m jI lA*jaa I? to 14} HiQf sohcol IrtgeiD IS to IS) Collegia................ .. .......... ......................... .. ...■ 6 N ol Including thla college, hour m any other colleges or universities hnve y ou ever aM ended’ ’ (Mark one i Nottii'I have otlBrrdefl crMy tms ooitegBi. 1 QllllV P ijo f h o m ................................... . . 4 orm o'c otno'f, . .. ................................... 7 How m any Credits have you earn e d at th is collage In previous sarrvestdrs? itWvk ut c, I M orte ................. -. ■ . . . . . . . . . . . 1.3 4 - 9 ................................................................................... ■ O - ' H 'C 2 7 .......................................................................... .. y r tv K s . ............. W-M t/om ’Icm 60 ................................... S S ince leaving high school h ave you ever taken c o u rse s at any other institution? Por Hot fo (M a'kajl that apply? C retin C redt voo, at ai'Otf O 'community or |on ot colloao . Yes, ,i! a 4-yr>iirco!icgi5 or university . . yog, at som - nmor pastsosoncary schosl (ter oriamnle i* hn cat. vocational, b u s m e s a i.......................... 9 In addition lo JMj college, are you liking course* at anoihar school or college thla semester? (Mntk pit that apply! vtp,, at arclhnr nommumlv coltnqe . t e u r a I'ni'.ynni oo.1 oijn inlvhteltv Y e s . a l a h ig h K C .h o a l................................ . . ver, at a vonalionnl o r trtirjo s o n o o '.......................... Y et.at an uauit school ............................ C X 3 C O * 5 a > £ ■ C I S 3 s ^ 5 2 e r ^ * S ' o s; a a . o ■ I m S’ 2 & g s » ^ § in c ip o *s -n &s <£ 5£ S 'S s a E ! -j a r I o CL ? ft a G s a * a 5 a 15 5 : > a > > < c 5 <s * i s P f s F fc, * O ' o> h m ?• • S ft w y J o : „ : © O 5- K £ ^ : « s © <7 * ? > ~ » £ » tu - » C O £ 5 { ? ► ?» 5^ I , M l i 5 5 r a $ *1 II 51 O _ o r 1 ? f t © S .* » « a ff 3 i “ 11 3 & | 5 | | i f q a I a Q > O f 1 ? | . o f f « 0 a ni a & ■ 0. g & P & 2. f- 0 P S C f C 0. =1 •5 0 » r * s 2 ^ s w > > S m V 3 0 •tJ 0 ' p . 0 Q •< S. S ^ ?? w .-> .■ » O: 21;=: f s-3 § i& ff _ - O m * * ~ & S 'S U a SS 9 3 ^ * ■ ’ tu 0 *5 it 0 0 •s h •» £■ I s. « 3- > 0 : a Z S 5 S 3- o a 5 ■ '399 ■^ | 5 • S f e $ cj m s< ? ti $ ® S 3 5 T a 5 3 ~ * to r> c <*1 t D tt * * » a < p O 5 to I S I : 3 ^ ; . D 0 L i 0 t j C 0 C 0 .0 0 ' L 1 ■ ’' , *1 J ' ■ j ; ; * ■ ! 00 c 0 0 0 r r ~ 0 0 ;. 0 0 0 C 0 Q fVo " 0 .....TTOiT Q e 2* D 3 „ » * 2 ! § i « |(J c » s II 0 e 0 0 0 a S' < M £? = e I SI S i« C _ T| — 4 1 * O O tt -z t5 cX D ti 3 g 5 5. i l f I f 3 s r a T > o 0 1 &isi *% fs •? ip m «i. S i‘ 3- F ■ 8 m < 0 8^ fit fi x - o o o o y 0 !T(jlT'P‘0 if^ V ni'TT^niy 0 s i f I If | | s 0 •» J » 1 i l l 2 * ffi i i * t s o ! S '°*i " I s is <a o t g - S rtw , 5 f | 0 ~ 2 5 a ® o i l l ! V? 31 j ___ f ' . » : J j., Q 6 0 1. ; n " , < * ■ r L/ w I I I I I i I t I I I I I I I I I I I I I B I I I 1 I I I I I I I I 1 I I I I I I I I I I I I i I I 1 I I 1 > 1 I I I 1 I I 1 o u > 104 18. H ow often do you u n a U n g iu g s o th e r th a n English with (he following p eo p le? (Mark on* for each statem an l) Vffln my paten ts............................... . ........... WHh Irlo n d s ..................................... w tn teachem or um iM w ra at this c o te g e 19. How well w e you able to do 1ha fallowing In gnallsh? (Marti one for each Item.) B e a d ................... ............. ...................... W rite ......................................... Understand a collage lemurs . .. .. R ead a co’ Jage taut boufc .................... Write an essay exam Wnie a term paper Participats lr o a s a d iscu ssio n s............ Communicate wifi n stm c to rs.............. 20. It E nglish your nativ e language? Y fe sa ______' G o to qusstlon 22 No . .' OonHri'je to question 21 21. H ow well are you ab le lo do th e follow ing In vour native ian g u ao o ? (Merit one for each ite m ) R o ad ............................................. Write . . Understand a eoltego le c t u r e ............. R ead a coioge text tjoou . ................ Write an essay e x a m ........................ Write a term p a o e r .................................... Participate m e a s a rtscusttisra . -. Communicate w th instructors.............. 12. How long d o es H take you to travel lo this college? (Marvone) L ess than 19 minutes .. 15 lo 30 m in u tes........... 3 i to <5 minutes 46 to 80 m inuses........... Between t and 2 hours • More than 2 hours 2 1 Do you have a disability? iMatk all trial apply.) H earing .................... ................................................... Speech McBHily im p aire d ................................... ........................ Attention deficit disorder ......... , .............. Psychctogcsi d is o rd e r............................... ............. Learning disability.................................................. '/.iron problem mat cannot be competed by glasses or contact tenses.............. - ........................ O ther................................................................................. No dsabikt!** .......................... ..... 24. W hat w a s your average grade In high school? (Marl; pro > A u r/it (Extmoidlrrary) . . . ............................... A- (Superior Q uality) ............. B* (Excellent!.................................................. . ■ B (Vsry Gootfl..................................................... ...................... B (Goodl . .......................................................................... C • lAbeve Average) ......... ................................................... C (Aversget , . C- (Betow A verage) . — ....................... D or lower (P o o ri................... • 25. B efore th is sem e ster, w hat m athem atics co u rse s have you ta k e n ? Include co u rse* In high s ch o o l or previous collage work. (Mark all that apply.) Basic morn, Business mnth or Pre-ftgehra Algebra I ................................. ........................... Geometry , ------- . ................................ . Algebra II Trgnnrtmeuy . . . Prc-cnlctjlus .. - ..................................................................... G a i c u l j s ............................................................... . . . . 26. Before this sem e ster. wliat scien ce co u rse s have you taken? Include c o u rs e s In high school o r previous college w ork. (Mark all thm appiy.) General Broogy ........................................................... Chomist7 , . , . . . . . . . ___ R hyses ................. - Biology epOCiolty y 0 nncrobolsgy. gontrt'cS. botany, cell K oogv. marine biology etc i — O ttte' Earth science (i.e.. gecfagy, riiBleoroiugy, e ( c . l ............. 27. With w hom d o you live while attending thla co lleg e? (Mark all that apply.) With my ip e u w or p a r t n e r ......................................... Wim my patents or guardians With my cmHrennstepcM dren........................ ................... With s.fcimgs (tjrclherrs) anaroreis1e r |s |) ...................... With other relatives ................................................ iMtn a roommateis) or a frlsnd(s) ........................— I :rvcmono . . . . . ........... 28. Your gender: Main ........................... . Parnate.................. ’ • 29. How o ld will you b e on O ocom bor 31 of th is year? I fE years or youngs' 1 ................................. . * B ........................................................... S C ...................................................................... £ 1-24 .................................................. 5 5 * 2 8...................................... . .. . . . 3 C -39 4C - W . ■ ................................. 55 o f o x i e r.............................................. — • • • • • ® • • -4- »n o I I I I I I I I I I I I I I I M I I I I I I I I I f i I I I I I I t I I I I I I I I I I I I I I 8 1 I I I I ■ I I I 1 I I I i s 3 ® I F £ 3 = n ft 2 E a J* - » « 2 c o » o 0 & JL c „ • 3 5 ^ £ if « JS ^ © «s ^ x * c d 9 a « S f c j 1 8 a a « j -a* > ^ ^ ft O C i* i e t e 5 ? O v ~ • V « ? X 1 a . o . v i 3 ^ S O’ 8 ^ 5 Q '5 • 9 9 9 9 9 9 ® - 8 2 E 5. r: * » tJ O . i e f t k ii ! c f 1 S i 1 is I | £ ? = -S g -3 = S- § ? c - C S I C - * * ir- g f *S I m i 13 f s S. a ^ c H i B £ 1 w « i» £ u » c ■ c £ & E2 I j i i "5* 5 £5 " * * * » 0 1 * § I jz c iJ a i i ? j- 6 S 1 1 ‘ £ i | : ? l l . l I E s f 1*1 5 I 9 3 a ft 5 5 c 6 3 s C J ^ r c P ^ F 1 ® 11 ; s : ‘ 2 S u $ S I E £ ■ e t h G. G Ui U . £ 1 1 o a E S3 IS I t 1 £ •= P £ £ 5 5 5 j= 3 5 S . 2 I i o G £ a 3 P £ g.e S' < ■ 11 s S ? £ I s ^ j t £ m 3 C 2.5 0 g * 5 If 3 * s g £ f t S i ? ^ V o s p I 1 1 & |V S. X T « Sf 5 ? ^ S £ p - 51? * s 9- s S'' 5 ft s I 1* £ | S T » t ffi Of •> 3 | O ” . .3 5 * O t> o c v ; r 0 * 0 t i a > a. £ E g 8 S > 8 i, 3 *2 is S > - 9 w s i i g 3 g O P. < h t o n o o o o " S « ? B § 5 5 a a q E F g P O P o o u c « : o n; j& P o Q o f 1 il O 2 p S 5 s f i l o o «r 5 . f! 5 £ a s $ ■ g - £ 11 e- » V £ 5 3 o c_ el F £ £ T o o p X o o • J e f i £ O S u. s 11 ” S s g -g p B o o a n 2 § c * I II I i I I I I I I I I I I 1 I I I I I IsI I I I I I I 1 I I I I I I I I I I 1 I I I I I I I K I I I I I II I I I I c f l 9 9 9 I I
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University of Southern California Dissertations and Theses
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Asset Metadata
Creator
Carpenter, Miki Nakasone
(author)
Core Title
Course completion and older adult students: The effects of self-efficacy, self -regulation, and motivation
School
Graduate School
Degree
Doctor of Philosophy
Degree Program
Educational (Educational Psychology)
Publisher
University of Southern California
(original),
University of Southern California. Libraries
(digital)
Tag
education, adult and continuing,education, community college,education, educational psychology,Gerontology,OAI-PMH Harvest
Language
English
Contributor
Digitized by ProQuest
(provenance)
Advisor
Hagedorn, Linda Serra (
committee chair
), Sundt, Melora (
committee member
), Zelinski, Elizabeth M. (
committee member
)
Permanent Link (DOI)
https://doi.org/10.25549/usctheses-c16-344278
Unique identifier
UC11340852
Identifier
3180374.pdf (filename),usctheses-c16-344278 (legacy record id)
Legacy Identifier
3180374.pdf
Dmrecord
344278
Document Type
Dissertation
Rights
Carpenter, Miki Nakasone
Type
texts
Source
University of Southern California
(contributing entity),
University of Southern California Dissertations and Theses
(collection)
Access Conditions
The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the au...
Repository Name
University of Southern California Digital Library
Repository Location
USC Digital Library, University of Southern California, University Park Campus, Los Angeles, California 90089, USA
Tags
education, adult and continuing
education, community college
education, educational psychology