CREDIT CARD KNOWLEDGE, ATTITUDES, AND
PRACTICES OF COLLEGE STUDENTS
by
ROSITA MOORE, B.A., M.A.
A DISSERTATION
IN
FAMILY AND CONSUMER SCIENCES EDUCATION
Submitted to the Graduate Faculty of Texas Tech University in Partial Fulfillment of the Requirements for the Degree of
DOCTOR OF PHILOSOPHY
Approved
Co-^airperson of the Committee
Co-Chairperson of the Committee
—f *-*—*—^—
Accepted
Dean of the Graduate School
August, 2004 © 2004, Rosita Patricia Welch Moore ACKNOWLEDGMENTS
I would like to express my sincere thanks and appreciation to Dr. Sue
Couch and Dr. Ginny Felstehausen, co-chairs of my doctoral committee, for their
guidance, support, and encouragement throughout this long process. Their
ability to work well as a team and their individual strengths were invaluable in
helping me complete this project.
I wish to extend special thanks to the other members of my committee, Dr.
So-Hyun Joo, Dr. Sue Reichelt, and Dr. C. Kenny Wu, for their expert advice and
encouragement. I especially wish to express my gratitude to Dr. Wu for
unselfishly giving of his time on evenings and weekends to provide advice and
assistance.
Special thanks go to Dr. Godwin Ashiabi, from the University of Missouri
Extension, who freely gave of his time and expertise as a statistical advisor. I
deeply appreciate his patience and guidance throughout this learning process.
My sincere thanks and appreciation go to Dr. Julie Middleton (University of
Missouri Extension) and Dr. Barbara Williamson (University of Missouri-
Columbia), whose willingness to hold me accountable made the completion of this dissertation possible. Without them I would not have stayed the course.
I extend special thanks and appreciation to my "family" from Cornerstone
Bible Church: Dr. Richard Grubbs, Dan and Sue Hanna, Condoy and Wanda
Hill, and Christopher and Lori Robinson. Their love and care for me, upon my return to Lubbock after an absence of four years, was a wonderful demonstration
of Christian love. They provided accommodation, meals, transportation, and
above all friendship, encouragement, support, prayers, and a belief that I could
do it. I am especially grateful to Dr. Grubbs, my pastor and friend, who has
encouraged and supported me from the very beginning of the doctoral program
and whose unfailing belief in my ability to succeed has been a great inspiration. I
could not have succeeded without his support. Special thanks also to Dan
Hanna for his provision of a place to study, a laptop, office supplies, and data
entry assistance. His incredible generosity made my success possible.
I also wish to express my thanks to the many friends and family who have given me support, encouragement, and assistance in various ways. Special thanks to Jim and Marilyn McMillian, Tom and Beverly Sowell, Dr. Lori Yoo,
Wesley Ingram, Juel Gibbons, Stacy Foster, Melody Zink, and Jacque Zuniga.
Most importantly, my deepest gratitude goes to my husband, Dr. C. L.
Wayne Moore, for his tremendous love and support. He has been a source of
inspiration and encouragement to me throughout this process and an invaluable help in countless ways. His great desire for me to succeed kept me from giving up, and his belief in my ability was constant. I am extremely grateful for his love and for all the sacrifices he made to help me complete the Ph.D. program.
Finally, I am thankful to my God and Savior for His provision of the stamina and strength to endure, the ability to succeed academically, and the love and support of my husband and friends. I am deeply grateful for His care.
Ill TABLE OF CONTENTS
ACKNOWLEDGMENTS ii
ABSTRACT vi
LIST OF TABLES viii
LIST OF FIGURES xi
CHAPTER
I. INTRODUCTION 1 Statement of the Problem 4 Purpose of the Study 6 Components of the Deacon and Firebaugh Framework 7 Research Questions 7 Definition of Terms 9 Basic Assumptions 12 Limitations of the Study 12
II. REVIEW OF LITERATURE 13 Theoretical Framework 13 History of Credit Cards 24 Current Trends 29 Research on Credit Card Users 36 Summary 53
METHODS AND PROCEDURES 55 Research Design 55 Design and Development of the Instrument 57 Selection and Description of the Sample 60 Data Collection 62 Data Analysis 62 Summary 70
IV. ANALYSIS AND INTERPRETATION OF DATA 72 Characteristics of the Sample (Input Variables) 72 Descriptive Statistics of the Throughput Variables 78 Descriptive Statistics of Output Variables 85
IV Results Related to the Research Questions 91 Summary 135
V. SUMMARY, FINDINGS, CONCLUSIONS, AND RECOMMENDATIONS 142 Summary of the Study 142 Summary of the Findings 147 Conclusions and Discussion 157 Recommendations for Further Research 169
REFERENCES 172
APPENDICES
A. COLLEGE STUDENT CREDIT CARD SURVEY In pocket
B. LETTER TO PROFESSORS/INSTRUCTORS 178
C. LETTERS OF PERMISSION 181 ABSTRACT
Widespread use of credit cards by college students has raised questions
about students' ability to manage their finances effectively. Parents, counselors,
college administrators, and others have expressed concern that young people
lack the necessary skills to handle the cards which credit card companies are
making increasingly accessible to college students.
The purpose of the proposed study was to obtain and analyze data on the
credit card behavior of undergraduate students enrolled at a major state-
supported university in the southwestern United States, with an enrollment of
approximately 27,000. Based on a revised theoretical framework of the Deacon
and Firebaugh model of family resource management, this study sought to depict
interrelationships between students' socio-demographic background (input),
college students' credit card knowledge and college students' credit card
attitudes (throughput); and college students' credit card practices (output).
Data were collected for the study in fall 2003 from a convenience sample
of 2,113 undergraduate students representing the nine colleges on campus. The
survey instrument used for data collection was developed by the researcher after
a literature search yielded no suitable existing instrument. A review of
questionnaires used in previous studies on college students and credit cards, as well as communication with researchers, generated concepts and questions for the development of an instrument. The questionnaire consisted of 39 questions
VI requiring a variety of responses, including true/false, ranking, fill in the blank, and
rating on a Likert-type scale.
Descriptive statistics were applied to determine how participants acquired
credit cards, their reasons for using credit cards, and the types of purchases
made with credit cards. In addition, descriptive statistics identified where
students had obtained their knowledge of personal finance/money management
principles; college students' attitudes toward financial education and counseling;
and college students' credit card, student loan, and other consumer debt.
Stepwise multiple regression analyses were conducted to examine the
relationships between the following: college students' socio-demographic
characteristics and college students' credit card knowledge and attitudes (input to throughput variables); college students' socio-demographic characteristics and college students' credit card practices (input to output variables) and between college students' credit card knowledge and attitudes and college students' credit card practices (throughput to output variables). A path analysis was used to test the conceptual model.
VII LIST OF TABLES
3.1 Definition of Variables Used in Multiple Regression Analyses 65
3.2 Factor Loadings for Questionnaire Item #13—Reasons Why College Students Use Credit Cards 67
3.3 Factor Loadings for Questionnaire Item #7—^Types of Credit
Card Purchase Items 68
4.1 Socio-Demographic Characteristics of the Sample 73
4.2 Socioeconomic Background of the Sample 74
4.3 College Students' Level of Credit Card Knowledge 79
4.4 Attitudes of College Students Who Use Credit Cards 81 4.5 Mean Values for Responses to Attitudes toward Use and View of Credit Cards Scale 82
4.6 Mean Values for Responses to Acquisition and Use of Credit Cards ... 84
4.7 Students' Attitudes toward Acquisition and Use of Credit Cards 85
4.8 Descriptive Statistics of the Output Variable 86
4.9 College Students' Reasons for Not Having Credit Cards 86
4.10 Reasons College Students Acquire Credit Cards 92
4.11 Reasons College Students Use Credit Cards 94
4.12 College Students'Level of Credit Card Use 95
4.13 College Students'Cash Advance Purchases 96
4.14 College Students' Sources of Financial Knowledge 98
4.15 College Students' Attitudes Toward Financial Counseling and Education 99
VIII 4.16 College Students'Debt Level 101
4.17 Regression Results with Socio-Demographic Characteristics as the Independent Variables and College Students' Knowledge of Credit Cards as the Dependent Variable 103
4.18 Regression Results with Socio-Demographic Characteristics as the Independent Variables and College Students' Attitudes toward Use of Credit Cards as the Dependent Variable 105
4.19 Regression Results with Socio-Demographic Characteristics as the Independent Variables and College Students' Attitudes toward Acquisition and Use of Credit Cards as the Dependent Variable 106
4.20 Regression Results with Socio-Demographic Characteristics as the Independent Variables and College Students' Reasons for Using Credit Cards (Convenience/Incentives) as the Dependent Variable ...109
4.21 Regression Results with Socio-Demographic Characteristics as the Independent Variables and College Students' Reasons for Using Credit Cards (Support for Lifestyle/College Education) as the Dependent Variable 110
4.22 Regression Results with Socio-Demographic Characteristics as the Independent Variables and College Students' Reasons for Using Credit Cards (Emergency/Credit Record) as the Dependent Variable 112
4.23 Regression Results with Socio-Demographic Characteristics as the Independent Variables and Number of Credit Cards as the Dependent Variable 113
4.24 Regression Results with Socio-Demographic Characteristics as the Independent Variables and College Students' Credit Card Purchases (Food/Clothing/Leisure) as the Dependent Variable 114
4.25 Regression Results with Socio-Demographic Characteristics as the Independent Variables and College Students' Credit Card Purchases (Education/Emergency) as the Dependent Variable 116
4.26 Regression Results with Socio-Demographic Characteristics as the Independent Variables and College Students' Credit Card Purchases (Housing Expenses/Cash Advances) as the Dependent Variable 118
IX 4.27 Regression Results with Socio-Demographic Characteristics as the Independent Variables and Number of Late Payments as the Dependent Variable 119
4.28 Regression Results with Knowledge and Attitudes as the Independent Variables and College Students' Reasons for Using Credit Cards (Convenience/Incentives) as the Dependent Variable 122
4.29 Regression Results with Knowledge and Attitudes as the Independent Variables and College Students' Reasons for Using Credit Cards (Lifestyle/College Education) as the Dependent Variable 123
4.30 Regression Results with Knowledge and Attitudes as the Independent Variables and College Students' Reasons for Using Credit Cards (Emergency/Credit Record) as the Dependent Variable 124
4.31 Regression Results with Knowledge and Attitudes as the Independent Variables and Number of Credit Cards as the Dependent Variable 125
4.32 Regression Results with Knowledge and Attitudes as the Independent Variables and College Students' Credit Card Purchases (Food/Clothing/Leisure) as the Dependent Variable 126
4.33 Regression Results with Knowledge and Attitudes as the Independent Variables and College Students' Credit Card Purchases (Education/Emergency) as the Dependent Variable 127
4.34 Regression Results with Knowledge and Attitudes as the Independent Variables and College Students' Credit Card Purchases Housing Expenses/Cash Advances) as the Dependent Variable 128
4.35 Regression Results with Knowledge and Attitudes as the Independent Variables and Number of Late Payments as the Dependent Variable 129 LIST OF FIGURES
2.1 Individual Personal/Managerial System 14
2.2 Theoretical Model Depicting Interrelationships between Input, Throughput and Output Components 19
2.3 Revised Conceptual Model Applied to Credit Cards 21
2.4 Model Depicting Interrelationships Between Input, Throughput and Output Components of College Students' Credit Card Knowledge, Attitudes, and Practices 23
3.1 Conceptual Model with Hypothesized Relationships between Variables: Socio-Demographics, Knowledge, Attitudes, and Practices 69
4.1 Proposed Path Analysis Model Linking Socio-Demographic Variables, Knowledge, and Attitudes to Card Usage and Total Debt 131
4.2 Alternative Path Analysis Model Linking Socio-Demographic Variables, Knowledge, and Attitudes to Card Usage and Total Debt 134
XI CHAPTER I
INTRODUCTION
Credit card usage by college students is an issue that over the past decade has gained increasing attention among researchers, educators, college administrators, financial counselors, the media, and others. There is growing concern that, while college students have easy access to credit cards, they lack the financial knowledge and skills to use credit wisely (Chen & Voipe, 1998;
Henry, Weber, & Yarbrough, 2001; Palmer, Pinto, & Parente, 2001).
Consequently, by the time they graduate, many young people have accumulated large amounts of debt and have essentially mortgaged their futures (Blair, 1997;
CBS, 2000; Quinn, 2001; Susswein, 1995).
College students and high school seniors, many of whom have "no job, no assets, no income, no credit history, and no means of supporting themselves"
(Susswein, 1995, p. 21), are the target of an aggressive marketing campaign by the credit card industry (Public Interest Research Groups (PIRG), 1998; Wanwick
& Mansfield, 2000). Direct mailings, telemarketing, advertising, and on-campus promotions, are used by credit card companies to entice financially inexperienced young people to become their customers. In addition, companies offer low interest rates, no annual fees, low minimum payments, a free T-shirt or other giveaways, and no cosigner requirement (Anders, 1999; Vickers, 1999). Credit card companies' marketing strategies appear to be successful. A
1998 study conducted by Nellie Mae, a leading provider of federal student loans,
revealed that 67% of undergraduate students surveyed possessed at least one
credit card, with 27% owning four or more cards. A similar study conducted in
2000 by Nellie Mae showed that 78% of undergraduates owned at least one card
and 32% possessed four or more cards. The average credit card debt among
undergraduates rose from $1,879 in 1998 to $2,748 in 2000. Thirteen percent of
undergraduates in 2000 had balances between $3,000 and $7,000 and 9% had
balances exceeding $7,000. The figures are even higher among graduate
students. In 2000, 95% of graduate students possessed at least one card, and their average balance was $4,776. Twenty percent had balances between
$6,000 and $15,000, while 6% had balances over $15,000 (Nellie Mae, 2002).
The easy accessibility college students and high school seniors have to credit cards has alarmed parents, credit counselors, and others who fear that students do not have the financial knowledge to handle such responsibility.
Studies appear to confirm this. According to a report by the Jump$tart Coalition for Personal Financial Literacy (1999), the average high school graduate lacks basic skills in personal financial management. Results of a 2002 Jump$tart
Coalition national survey of high school seniors' financial knowledge revealed a steady decline in average scores over a five-year period. The average score of participants surveyed in 1997 was 57.3%; in 2001 it was 51.9%, and in 2002 the average score was 50.2% (Jump$tart, 2002). Another nationwide study, America's Money Skills Report Card, conducted on behalf of Americans for Consumer Education and Competition (ACEC),
revealed that high school seniors were deficient in their knowledge of personal financial issues (ACEC, 2001). Only 4.52 of the 13 questions, or 34.6%, were answered correctly by the 800 high school seniors who participated in the survey
(ACEC).
Such studies reveal that the need to educate young people in financial literacy is acute. Too often, financial education and counseling take place after individuals have become so deeply in debt that they are unable to meet their financial obligations. This is the opposite of what should occur. Young people need to be equipped with financial competencies before they are required to make decisions regarding the use and management of money. Since it is their responsibility "to create the necessary support systems to encourage the academic success and personal success of their students" (Pinto, Parente, &
Palmer, 2001b, p. 172), college and university administrators need to provide students with opportunities to receive financial literacy training.
Borrowing money and the use of credit cards have become firmly entrenched in the American culture. As Manning (2000) points out, America has become a "credit card nation." Easy access to credit cards and the misuse of them pose a threat to the economic and academic wellbeing of college students.
Universities must respond to the challenges facing students by providing educational programs to meet their needs. Counseling centers, orientation programs, academic courses, seminars, and workshops may be utilized
effectively to provide the necessary personal financial literacy training to students
on the nation's campuses.
Statement of the Problem
The widespread use of credit cards by college students has raised
questions about students' ability to manage their finances effectively. Parents,
counselors, college administrators, and others have expressed concern that
young people lack the necessary skills to handle the cards which credit card
companies are making increasingly accessible to college students. Several
researchers have focused on the extent of credit card usage by students and
have concluded that the majority of college students use credit cards, with some
carrying substantial balances each month (Hayhoe, 2002; Joo, Grable, &
Bagwell, 2003; Manning, 2000; Nellie Mae, 2000; Quinn, 2001).
More research is needed on a broad range of concerns related to college
students' credit card practices. For example, what entices students to apply for
credit cards? Do students obtain cards because they find it impossible to resist credit card marketers on their campuses? Are they "hooked" by marketing techniques on the Internet, the telephone, or by mail? What motivates students to use credit cards? Do students charge items on their credit cards because of convenience, because they are attracted to goods on the Internet, or because they need to supplement their income with credit card purchases and cash advances? What types of purchases do students generally make with credit
cards? Do students use credit cards to pay for education-related expenses?
Are they using cards for monthly living expenses? An understanding of these
and other issues is necessary for college administrators, educators, and
counselors who are concerned with addressing the financial literacy needs of
college students.
Current data on students' credit card knowledge, attitudes, practices, and
debt level on individual campuses will provide administrators and counselors with
a clearer understanding of the needs of their student population. This will assist
them in determining the best approaches to combat the problem of college
students and irresponsible credit card behavior.
Administrators at Texas Tech University (TTU) are concerned about their
students' management of personal finances (D. Bagwell, personal
communication, October 29, 2001). Anecdotal evidence suggested that some
TTU students were struggling with debt and the inability to fulfill their financial obligations. Administrators believed the problem was acute enough to warrant intervention by the University. To address their concern, a multi-organizational program. Red to Black, was established in 2001 to provide financial education and counseling for TTU students. The Red to Black program is a partnership of the Vice-President for Student Affairs, the TTU Personal Financial Planning program, and the Center for Financial Responsibility. Red to Black is aimed at helping students "reduce and avoid red ink in their personal finances and stay in the black through increasing education and responsible financial behavior" (D.
Bagwell, personal communication, October 3, 2002). To date. Red to Black has
trained 20 peer counselors and 22 peer educators to provide individual peer to
peer financial counseling and financial education to TTU students (D. Bagwell,
personal communication, April 22, 2004).
There is need for more data on TTU students' credit card behavior and
their level of credit card debt. A better understanding is needed of TTU students'
credit card knowledge, attitudes, practices, and debt level. This study not only
provides insight into the financial behaviors of the student population at TTU, but
it also helps to provide a rationale for continued funding of the Red to Black
program as the need for such a program is validated. It offers practical
information for the administrators, educators, and counselors of the Red to Black
program as they seek to meet the needs of students by developing and
delivering educational programs and effective counseling approaches to address the challenges faced by TTU students.
Purpose of the Study
The major purpose of the study was to provide data on the credit card
knowledge, attitudes, and practices of undergraduate students enrolled at TTU, a major state-supported university in the southwestern United States, with an enrollment of approximately 27,000 students. In addition, the research sought to add to the body of literature by utilizing a revised theoretical framework of the Deacon and Firebaugh (1988) model of family resource management The study
sought to depict interrelationships among students' socio-demographic
background (input), college students' credit card knowledge and college
students' credit card attitudes (throughput); and college students' credit card
practices (output).
Components of the Deacon and Firebaugh Framework
The Deacon and Firebaugh (1988) framework posits three components:
input, throughput, and output (see Chapter 2). Input represents factual and
existing variables, throughput depicts the change process that effects output, and
output displays the final results of the framework from which the only change that
can be realized is an alteration of the input component. In this study, input is
represented by the independent variable (socio-demographic characteristics).
Throughput consists of college students' credit card knowledge and college students' credit card attitudes. The throughput component consists of mediating variables because they indirectly explain the relationship between the independent variable and the dependent variable. Output, the dependent variable, is represented by college students' credit card practices.
Research Questions
Based on a review of the literature and the purpose of the study, the following research questions were addressed: 1. How did college students acquire their credit cards?
2. What reasons do students give for using credit cards?
3. What types of purchases do students make most frequently with
credit cards?
4. Where do college students obtain their knowledge of personal
finance/money management principles?
5. What are college students' attitudes toward financial education and
counseling?
6. What is the debt level of college students as it relates to:
a. credit card debt
b. student loan debt
c. other consumer debt?
7. What is the relationship between selected socio-demographic
variables (age, gender, ethnicity, marital status, classification,
major, grade point average, employment, income, sources of
income, and socioeconomic background) and
a. college students' credit card knowledge
b. college students' credit card attitudes?
8. What is the relationship between selected socio-demographic
variables (age, gender, ethnicity, marital status, classification,
major, grade point average, employment, income, sources of
income, and socioeconomic background) and college students'
8 credit card practices (reasons for using credit cards, number of
cards, types of purchases, repayment practices)?
9. What is the relationship between:
a. college students' credit card knowledge
b. college students' credit card attitudes and
college students' credit card practices (reasons for using
credit cards, number of cards, types of purchases,
repayment practices)?
Definition of Terms
The following terms are defined for the purposes of the study:
Attitudes. Beliefs, feelings, preconceived notions or action tendencies of individuals toward credit cards and their use (Punjavat, 1992; Sholten, 1981).
Cash advance. A cash-loan from a bank credit card account.
College student. A person over the age of 18 who is enrolled in a course of study at a four-year college or university.
College student credit card behavior. Students' credit card knowledge, attitudes, and practices.
Component A cluster of variables in a box, representing the input, throughput, and output portions of the theoretical model on which this study is based (Tucker, 2000). Consumer education. Curriculum designed to improve personal decision making regarding finances.
Credit card. A card issued by a bank, financial institution, or retail business that allows the cardholder to charge goods and services or borrow money. The balance may be paid in full within one billing cycle or carried over by making partial monthly payments (Brundage, 2001). In this study, "credit cards" is the umbrella term used for bank, travel and entertainment gasoline, and department store cards.
Credit card practices/usage. An individual's possession, utilization, purchasing-habits, and payment practices with respect to credit cards.
Entering Freshman. Undergraduate student who entered TTU in the fall
2003 and who had no grade point average at the college level.
Freshman. Undergraduate student who had been enrolled at TTU for more than one semester and had completed less than 30 semester hours of course work.
Input. Matter, energy, and information entering a system in various forms to affect individuals' personal and managerial reactions (throughput) in the achievement of outcomes or output (Deacon & Firebaugh, 1988). In this study, the input variable is college students' socio-demographic characteristics.
Knowledge. Individual's level of intellectual understanding and comprehension of credit cards and their use.
10 Marketing strateoies/technioues. Methods used by credit card companies to attract college students to fill out credit card applications.
Mediating Variable. A variable that acts as a dependent variable in
relation to the independent input variable, while it acts as an independent
variable toward the dependent, output variable (Tucker, 2000).
Output Matter, energy, or information produced by a system in response
to input and throughput (transfomiational) processes (Deacon & Firebaugh,
1988). Students' credit card practices is the output variable in this study.
Personal finance. The study of personal and family resources considered
important in achieving success (Garman & Forgue, 1997, p. 3). For the purpose
of this study, personal finance will relate especially to the practice of borrowing,
particularly by means of credit cards.
Personal financial literacy. The ability to manage one's personal finances successfully. Lack of financial literacy may result in excessive levels of debt
(Garman & Forgue, 1997).
Throughput The transformation of matter, energy, or information by a system from input to output (Deacon & Firebaugh, 1988). In this study, throughput consists of college students' credit card knowledge and college students' credit card attitudes.
11 Basic Assumptions
The following basic assumptions underlie the study and the interpretation of the results:
1. Students who completed the survey did so thoughtfully and
accurately.
2. Students who participated in the study during fall 2003 were not
significantly different from those students who did not complete the
questionnaire.
Limitations of the Study
The following limitations of the study were recognized with regard to the interpretation of the results of the study:
1. The sample was limited to students at TTU who were enrolled in
the fall 2003 semester. Results may not be generalized to students
across the United States.
2. Instrumentation was limited to self-report measures. Therefore, the
instrument measured only what individuals perceived about their
own credit card knowledge, attitude, and practices and what they
were willing or able to disclose.
12 CHAPTER II
REVIEW OF LITERATURE
A review of the literature provided a theoretical framework for the study as
well as insight and background information related to college students and credit
cards. This chapter is divided into four major sections: Theoretical Framework,
History of Credit Cards, Current Trends, and Research on Credit Card Users.
Theoretical Framework
The framework for the study was based on family resource management
theory as developed by Deacon and Firebaugh (1988). According to the
theorists, management is a basic tool for achieving "meaningful, effective living of
individuals and families" (p. 3). It helps control the events of life and influences the outcome. Living involves responding to continuous change, and individuals
need to anticipate change and apply management processes to direct their daily
lives. These management processes will vary depending on the backgrounds of individuals.
In their conceptual model of the individual personal/managerial system,
Deacon and Firebaugh (1988) identified three components: input, throughput and output (Figure 2.1). Input is defined as "matter, energy, and/or information entering a system in various fonns to affect the throughput (transformation)
13 c g (0 o "5. Q. < c CO Is a^ . (D>-•" >
£=5 0) -o O) CD CD CO E *- 2 B S>« CO J3 0) 0 Q_ |ro :- '^ '^ U- O (U : c O) S O (0 c <» 52 CO = '^'C £i_ s CO O V >• LV_) o8 J3 0 c -a Q 14 processes resulting in output" (pp. 8-9). Input consists of demands and resources, and these may originate from either inside or outside the system. Demands, as defined by Deacon and Firebaugh (1988), are either events or goals that require action. They are "inputs that provide the stimulus, motivation, and meaning to the activity undertaken within the system" (p. 16). Events may be unexpected, at times unpleasant, occurrences that require action. These may be of major or minor proportion and may originate inside or outside the system. Events may be perceived differently by different families depending on the values and resources of each family. For example, an event may be perceived by one family as minor and by another family as major. Most accidents are events which cause adjustments, or at times redirection, to the flow of life. In contrast, goals are value-based objectives or anticipated outcomes. Goals may change over time, although values remain stable. Values are essential meanings, related to what is desirable or has worth. They provide the fundamental criteria for goals, thereby ensuring continuity in decisions and actions. According to Deacon and Firebaugh (1988), events and goals merge as inputs, forming the demands that give direction for activity in the throughput component of the personal/managerial system. Resources are human and material means that provide the characteristics or properties capable of meeting the demands placed upon the system by goals and events (Deacon & Firebaugh, 1988). They are necessary for the family to 15 solve every management problem. Human resources are the skills, abilities, and knowledge within people that can be utilized in meeting demands. They consist of personal characteristics such as health, energy, time, cognitive insights, affective attributes, and psychomotor skills. Material resources are the non- human means for meeting goals and events. These include goods, money, housing, household capital, and investments. The family's resources are constantly changing as resources are used and new resources are developed or acquired (Deacon & Firebaugh). Throughput the second component in the model, is the planning and implementing of resources to meet demands. According to Deacon and Firebaugh (1988), planning is a "series of decisions concerning future standards and /or sequences of action" (p. 10). It is a process where individuals and families use cognitive skills to envision what is to be done. Implementing, on the other hand, is putting plans into effect and checking or comparing actions with plans and making adjustments, as needed, to reach the desired outcomes (Deacon & Firebaugh). Deacon and Firebaugh (1988) defined throughput as the "transfomnation of matter, energy, or information by a system from input to output" (p. 10). According to the theorists, although the effectiveness of a system may be measured by comparing its input and output, it is important to have a clearer understanding of the internal processes by means of throughput to avoid arriving at misleading conclusions. Deacon and Firebaugh acknowledged that the 16 throughput processes may be entirely different within various systems. For example, two families may experience varying initial circumstances or conditions, but the throughput process may lead them to the same outcome. On the other hand, similar opportunities and beginning circumstances may lead to completely different outcomes for two families. Deacon and Firebaugh suggested that these differences among families may be explained by "differences in their adaptive responses to other systems and to their decisions as growth-supporting units" (p. 17). The third component, output, is described as matter, energy, and/or information produced by a system in response to input and from throughput processes. Outputs in a managerial system are demand responses and resource changes resulting from transformation (throughput) in response to demand and resource inputs (Deacon & Firebaugh, 1988). Demand responses are the output relating to values and satisfaction, while resource changes are the output relating to the stock of human and/or material means. According to Deacon and Firebaugh (1988), feedback is that part of output that reenters a system as input to affect later transformation processes and/or output Feedback "helps maintain the dynamic nature of an open system" (p. 123). It is the monitoring or reprocessing of the output, and it may be positive or negative. Positive feedback accepts deviations from the expected results and promotes change. Negative feedback, on the other hand, performs a monitoring 17 function and seeks to reduce deviation when there is a difference between actual and desired output. A thorough review of the Deacon and Firebaugh (1988) theory of family resource management and its individual personal and managerial system, as well as a review of other literature, indicated that a revised Deacon and Firebaugh model would be most appropriate for the purpose of this study. Two studies (Punjavat, 1992; Tucker, 2000) were investigated as possible sources for a revised model of the Deacon and Firebaugh framework. In her study of the processes involved in baby boomers' financial preparation for retirement. Tucker (2000) examined relationships between the input (independent) variables, the throughput (mediating) variables, and the output (dependent) variables (Figure 2.2). Tucker's input variables consisted of baby boomers' demographic characteristics, including age, gender, ethnic background, marital status, household income, and educational attainment. Throughput variables (anticipated age of retirement, confidence in social security, and retirement savings calculation) were identified by Tucker as personal and managerial responses that were expected to mediate the effects of input on the output They "provide the functioning mechanism through which families and individuals can plan for retirement income" (p. 17). According to Tucker, the mediating variable acts as a dependent variable in relation to the independent, input variable, while it acts as an independent variable toward the dependent, output variable. In the Tucker revised model, output variables consist of baby 18 Output Dependent Variables Input Independent Current amount of Variables retirement savings Throughput Expected sources of Age Mediating Variables retirement income Money provided by Gender Anticipated age of employer retirement Money you put into Ethnic K N retirement plan at background work Confidence in Social ) Security }/ / Other personal Marital status savings Retirement savings Sale of home or Household calculation business income Part-or full-time employment Educational attainment Children or other family members Social Security Other government benefit programs Post-retirement employment plans To make ends meet To buy extras Try a different career Enjoy and want to stay involved Support children and other family members Health insurance or other benefits 1 Figure 2.2. Theoretical Model Depicting Interrelationships between Input, Throughput and Output Components. Source: Jeanette Adcock Tucker, "An Examination of the Baby Boom Generation's Financial Preparations for Retirement," 2000. Used by permission of author. 19 boomers' current amount of retirement savings, expected sources of retirement income, and post-retirement employment plans. The overall purpose of the Tucker (2000) study was to investigate the baby boomers' financial preparation for retirement by determining the relationships between baby boomers' input variables and the throughput variables, between input variables and output variables, and between throughput variables and output variables. The purpose was achieved by statistical testing of hypotheses relating to input to throughput variables, input to output variables, and throughput to output variables. Punjavat (1992) based a study of international graduate students and credit cards on a revised model of the Deacon and Firebaugh (1988) conceptual framework (Figure 2.3). In Punjavat's study, input consists of four independent variables: socio-demographic characteristics, credit card knowledge, credit card experiences, and credit card attitudes. According to Punjavat, socio- demographic characteristics may be categorized as input as they are the resources upon which students draw when making decisions about credit card practices. Knowledge regarding credit cards and experiences with credit cards are internal (human) resources, and a higher level of credit card knowledge should result in more efficient use of credit cards, while positive or negative experiences should impact practices. Attitudes toward credit cards are considered by Punjavat to be a demand input and should influence the planning and implementing of credit card use. 20 •D c cti o Ca r >•-• a CM I- •o (0 (1) H-> iZ. O CeOn J2 (1) E (0 c o o (0 o c •D 0) 0 TD 3 0) +^ Li. CO 0) (D Q. •o 3 b f) T3 X CO de s 0) M- ed g O c o •*-> 4-* 3 - o ^0) •^^ ^^^ O "tS'« -a 3 3 O CO 21 In the Punjavat (1992) study, throughput consists of credit card practices, which involve both planning and implementing credit card use. Credit card practices are responsible for transforming input to output (credit card satisfaction) and may be described as an intervening variable. Output is the dependent variable. Unlike Tucker (2000), the Punjavat model includes feedback as a fourth component. A high level of satisfaction would re-enter the system as positive feedback, while a low level of satisfaction would re-enter as negative feedback. Negative feedback would promote necessary changes. The revised model of the Deacon and Firebaugh (1988) framework, upon which this study is based (Figure 2.4), has been strongly influenced by both Punjavat (1992) and Tucker (2000). It differs from the Deacon and Firebaugh model only in its simplicity. The major components, input, throughput, and output, remain the same; however, more detailed categorization is omitted. Input, the external or internal demands and resources that enter the system, thereby allowing the transforming processes to produce output, are represented by the independent variable, college students' socio-demographic characteristics. The socio-demographic characteristics that influence college students' credit card knowledge and credit card attitudes (throughput) were selected from the body of literature and consist of age, gender, ethnicity, marital status, college classification, major course of study, grade point average, employment, income level, sources of income, and socioeconomic background. The mediating variables (college students' credit card knowledge and credit card 22 0) s O) •Q ts b CO O °? D ^ CO OJ 1— it C 1- c oti c :3« ^2 OS (J°- o a? Q. 0) Q O c *- o Q. (0 li CO M- (0 0)-o s< ud e -.9 +-• 'H-» 3 O Car d Variai Q. (D GH P ledg e d Atti t 3 O) jEi Ca n redi t no w 0) c 9l ^-> ^1 O^ 73 HX S0) CD CD -o o (0 c 0) o G) JO 0) O ?1S ^ N^ ^- — 0) o O i_ 0) o Q. •O CM Q riabI tic s rap h •S0 O TJ t-2 O (0 ^3 <«>> c > cteri s 2mo g (0 0- S . TJ Z"? n 2 3 — c cvi w Q. cio - Ch a •0 o 3 5 CO 23 attitudes) are managerial processes that affect output (credit card practices). Credit card practices in the current study involve the acquisition, possession, and usage of credit cards. The term encompasses the reasons for using credit cards, the number of cards owned, types of purchases made, repayment practices, and credit card debt level. History of Credit Cards The credit card industry is an American innovation, designed to meet the social, economic, and technological characteristics of America. With its large land area, geographically dispersed population, strong industrial base, and its belief in capitalism, the United States readily embraced the concept of credit and the credit card (Mandell, 1990). The first credit cards were issued by hotels in 1900 (Meier, Garman, & Keiser, 1998), and by 1914 a few large department stores were also issuing cards to their most highly valued customers (Galanoy, 1980). These early cards were designed to identify customers with charge accounts and to provide a means of keeping a record of customer purchases. They were a convenience and a source of prestige to the wealthy customers who owned them. By 1928, department stores began issuing metal "charga-plates," embossed with customers' addresses and functionally similar to today's cards. Over the next 30 years other innovations followed, such as minimum monthly payments, finance 24 and entertainment purchases. By 1958, this "universal card" had spread across the United States and had been joined by Carte Blanche and American Express (Evans & Schmalensee, 1999; Hendrickson, 1972; Mandell, 1990). The post World War II decade also gave rise to the creation of the bank credit card, which is now of greater importance than any other kind of credit card operation (Hendrickson, 1972). In 1958 the country's two largest banks. Bank America of San Francisco and Chase Manhattan Bank of New York introduced credit card programs, and by 1959, 150 banks were in the credit card business. Banks followed the example of the retailing industry in introducing revolving credit and finance charges on unpaid balances (Mandell, 1990; Ritzer, 1995). In 1966 Bank of America decided to license its BankAmericard across the country. Several large banks quickly joined together to form a second national card system, the Interbank Card Association, which later bought the rights to MasterCharge from the Western States Bank Card Association. By 1979 BankAmericard and MasterCharge had become Visa and MasterCard respectively, and more than 11,000 banks had joined these two networks (Ritzer, 1995). Annual sales were over $44 million, with 52 million Americans owning at least two bank credit cards (Mandell, 1990). Competition in the credit card industry was keen throughout the 1970s. In an attempt to gain the upper hand over each other, both Visa and MasterCard had prohibited the issuing of both cards by the same bank. The courts, however. 26 declared this exclusion policy to be a violation of anti-trust laws, and banks were forced to accept duality from the mid-1970s (Mandell, 1990). Banks also were in competition with the retail industry, which had been operating its credit card systems throughout the 20*'' century, issuing more cards than all the third-party companies combined. In 1979, however, J.C. Penney became the first major retail chain to sign an agreement with a credit card company, allowing Visa cards to be accepted in Penney stores (Manning, 2000). Gradually major stores began to accept third-party credit cards in an effort to increase business. Throughout the 1970s and into the 1980s, the bank credit card industry expanded rapidly, both nationally and internationally. However, from the beginning, the bank card companies had struggled to make a profit. Despite a growing number of merchants and more and more credit card holders, the banks actually lost money from 1979 to 1981 when the country was battling inflation (Mandell, 1990). Mandell pointed out that huge start-up costs; processing costs; restrictive usury laws, which prevented the raising of interest rates; and large losses caused by fraud prevented the credit card companies from effectively controlling their costs. In addition, market pressures prevented them from imposing an annual fee, which would have increased revenues. To maximize revenue, the banks began to use an average daily balance system to calculate interest rates, rather than the adjusted balance method, which calculated interest on the balance at the time payment was due (Mandell, 27 1990). Marketing various products and services, such as insurance, with monthly statements and marketing return envelopes to advertisers were among some of the other methods used by banks to increase revenue. They continued to struggle, however, until the end of the decade when several states lifted or eased their usury ceilings and allowed the banks to raise their interest rates. In addition, federally imposed credit controls gave the banks the excuse they needed to impose annual credit card fees (Mandell). The decade of the 1980s was "a period of tremendous growth for the credit card industry, in both profits and cardholders" (Mandell, 1990, p. xx). By 1986 more than 55% of families in the United States owned a bankcard, more than three times the number for 1970. The number of participating banks rose from 71% to 90% (Mandell). As the banks expanded their operations, competition became fierce. According to Ritzer (1995), "The credit card companies have been caught up in a hard law of capitalism: Either they must continually expand, or they will decline" (p. 42). One way in which credit card companies have attempted to find new business is through highly publicized charities. For example, in 1984 American Express pledged to donate one dollar for each new card and one cent of every card transaction to the restoration of the Statue of Liberty. In three months, new card applications rose by 45%, and usage of American Express cards was up by 28% (Ritzer, 1995). 28 Another strategy is for credit card companies to use the "secured" credit card to reach the high-risk market. Companies not only obtain collateral from the customers for the card, but they also charge higher interest rates and fees to a population that is often willing to pay for the privilege of getting a card. The earnings from secured credit card accounts may be in excess of twice the earnings from unsecured accounts (Ritzer, 1995). Current Trends A current strategy of credit card companies is to entice college students into owning their own credit cards. Credit card companies have been aggressively marketing to this population because of the immediate increase in business as well as the long-term income possibilities (Manning, 2000; Ritzer; 1995). Companies such as Citibank and American Express Optima market credit cards on approximately 1,000 campuses (Anders, 1999, PIRG, 1998). One of the most effective marketing techniques by credit card companies has been to issue affinity cards which carry the Visa or MasterCard name, as well as the name of another entity (Mandell, 1990; Ritzer, 1995). One type of affinity card, the "lifestyle" card, is designed to attract members of a particular organization, usually having a substantial number of members or affiliates. Affinity cards have become popular with administrators at universities across the country, who see a way to increase their revenue by receiving "kickbacks" from banks (Manning, 2000). 29 One of the most lucrative contracts was signed in 1998 when the University of Tennessee signed a seven-year, $16 million contract with First USA. This deal gave First USA the sole right to market the University's Visa "affinity" card, with the school's picture and logo, to its 26,000 students, 270,000 alumni, and untold numbers of sports fans. In addition to the $16 million, the University receives 0.5% on every transaction charged to the credit cards, an estimated $4 million a year (Vickers, 1999). In 1999 the release of the results of a national survey on credit card debt and the testimony of parents of two students whose credit card debts had contributed to their suicides (Manning, 1999; 2000), triggered an outcry against credit card marketing on college campuses. Acknowledging the vulnerability of college students, many college administrators have restricted on-campus marketing of credit cards to students (Anders, 1999; Wanwick & Mansfield, 2000). In fact, during the 2000-2001 academic year, more than 800 colleges and universities in the United States developed policies restricting credit card marketing on campus (Manning, 2000). However, administrators at the 250 largest public universities in the nation continued to enter into lucrative contracts with credit card companies. Faced with fiscal problems often resulting from inadequate state funding, many college administrators are willing to sacrifice the interests of their financially naive students to gain the credit industry's big dollars (Manning, 2000; Vickers, 1999). 30 Manning (2000) estimated that over a five-year period banks would pay the largest 250 universities nearly $1 billion annually for exclusive marketing rights to their student population. Although it is uncertain whether, with the current economic crisis facing the nation, this still holds true, there are several reasons why companies were willing to make such a large investment in this market. First, while students appear to be a high credit risk, they are, in fact, a very low-risk group (Markovich & DeVaney, 1997). Although students are usually unemployed, or under-employed, and their credit is unsecured, parents often will come to the rescue when their children run up more debt than they can handle (Susswein, 1995). Second, college students are proving to be "an even more profitable and strategically important market than their baby boomer parents" (Manning, 2000, p. 167). Mass marketing to students on campus costs about half what it would take to target their parents, and it is highly unlikely that the college student market will become saturated as it is replenished each year by freshmen and transfer students (Manning, 2000). Third, credit card issuers acknowledge students' potential for future earnings (Blair, 1997). Although college students' incomes are relatively low, their expected future income will greatly exceed that of employees who have only a high school diploma. Fourth, research shows that Americans are loyal to their first credit card, keeping it an average of 15 years (Marcus, 2001; Vickers, 1999). Therefore, each bank wants its card to be the first a college student acquires. 31 Once a bank secures a student customer, it can market other products such as car loans, first mortgages, and even debt consolidation loans to help repay credit card loans (Vickers, 1999). Finally, students are not merely a source of potential revenue after they graduate; they are big spenders while in college. Student Record, a market research group, reported that undergraduates spend more than 21.6 billion dollars a year (Marcus, 2001). Credit card issuers want a share of that revenue. In attempting to gain additional revenue from college students, banks and other corporations, such as American Airlines, Wal-Mart, Time, and MCI, have worked together in lucrative "cross-marketing" opportunities aimed at the student consumer (Manning, 2000). There is an ongoing debate concerning whether credit card issuers should be blamed for college students' high degree of indebtedness. On the one hand, it has been argued that credit card issuers put cards in the hands of financially irresponsible young people and this leads them down a path of financial self- destruction. Young people are portrayed as being hunted or as fish being lured and then hooked (PIRG, 1998; Manning, 2000). However, others believe students are totally responsible for the misuse of credit cards that issuers merely make available to them. College, they argue, prepares students for learning to be independent and personally responsible; it is a time to learn how to set spending limits and to live within one's means (Smith, 1999). 32 College students are obtaining their first credit card at a younger age. According to a 1999 Consumer Federation of America (CFA) Press Release, whereas in 1994, 66% of students received their first card before college or in their freshman year, in 1998, 81% had received their first card by the end of their freshman year (CFA, 1999). A recent study by Joo et al. (2003) revealed that on average students obtained their first card at age 18, and some students had received their first card as young as 15 years old. This is not surprising since high school students increasingly became targets of the credit card companies during the 1990s (Ritzer, 1995). In 1993, 32% of high school students had their own credit cards, and efforts were being made by companies to increase this percentage (Ritzer). Marketing efforts by credit card companies included annual supplements sponsored by Discover Card in magazines aimed at high school students as well as financial education programs sponsored by Visa. These programs, although not specifically identifying Visa as the sponsor, have caused concern among consumer advocacy groups and others who recognize that credit card companies are in the business to profit from students' credit card usage. According to Ritzer, by allowing credit card companies to teach financial management to high school students, school administrators and teachers are relinquishing their responsibility. To address this deficit, several organizations have developed financial literacy initiatives targeted toward the nation's youth. For example, private 33 organizations, such as Jump$tart Coalition for Personal Financial Literacy and the National Endowment for Financial Education (NEFE) have developed financial education curricula for high school students. An evaluation to determine the impact of one such curriculum was undertaken by NEFE in collaboration with the U.S. Department of Agriculture Cooperative State Research, Education, and Extension Service. Results indicated that "students can and do respond positively to instruction aimed at improving their money management skills" (NEFE Press Release, 1998). Family and Consumer Sciences (FCS) courses at the high school level across the country offer instruction in personal financial literacy. Family and Consumer Sciences educators have been providing financial education in high schools for decades. In the 1990s, the National Association of State Administrators for Family and Consumer Sciences spearheaded a movement to develop national standards for FCS content areas. This has resulted in "contemporary curriculum content and delivery system" (Smith, Hall, & Jones, 2001, p. 49). The national standards are comprehensive and broad in scope. Each area of study has a comprehensive standard that provides a broad description of the content area. Each comprehensive standard contains two to eight content standards which identify the knowledge and skills to be learned. Competencies further define the content standards and provide the basis for evaluation (NASAFACS»V-TECS, 1998). The area of study which addresses the need for 34 students to be skilled in personal financial management is Consumer and Family Resources. Students are expected to be able to "demonstrate management of financial resources to meet the goals of individuals and families across the life span" (NASAFACS»V-TECS, 1998). Specifically, students are expected to "examine the need for personal and family financial planning" and to "apply management principles to individual and family financial practices" (NASAFACS«V-TECS). The national standards do not dictate curriculum. They serve as a guide to the individual states in developing their specific curriculum guidelines (Pullen, 2001). Actual FCS content varies across states according to educators' perceived needs of students. Reichelt's (2001) survey of family and consumer sciences state administrators, representing 44 states, revealed that family and consumer sciences national standards were strongly supported at the state level. Of the 44 head state administrators interviewed, 93% indicated that the national standards were being implemented in their states. The motivations for implementation cited most often were to improve curricula (78%) and to aid in developing new curricula (63%). A study by Pullen (2001) revealed that in the state of Massachusetts family and consumer sciences professionals perceive consumer and family resources to be an important area of study. Of the16 family and consumer sciences areas of study, consumer and family resources was rated seventh in order of importance by head state administrators. Among the 44 head state 35 administrators in the Reichelt (2001) study, there was consensus that consumer and family resources was one of the nine areas of study considered most central to programming in their individual states. In Texas the family and consumer sciences curricula for both middle and high school students contain requirements for students to be taught personal financial management skills. The middle school course Skills for Living, provides opportunities for students to be exposed to personal management principles. Students are expected to "utilize the decision-making process and goal setting to guide spending; and apply consumer practices facilitating the best use of available funds" (TEKS, 1998, p. 40). At the high school level, taking a course in Management should expose students to the importance of managing time, energy, and money resources and enable them to "identify the components of money management" (p. 96). A second course, Consumer and Family Economics, focuses on the management of financial resources, and students are expected to "develop consumer and financial management expertise" (p. 98). Research on Credit Card Users The phenomenal growth of the credit card industry among young people has stimulated interest by researchers in the credit card practices and behavior of college students as well as in the background and attitudes of college students who own and use credit cards. For example, researchers have sought answers to the following questions: Which socio-demographic variables have an impact 36 on college students' credit card behavior? Which students would be most likely to own and use credit cards? How frequently do college students use credit cards? What are the attitudes of college students toward credit cards? What is the knowledge level of college students regarding credit cards? Credit Card Knowledoe Surveys show that American teenagers and adults have inadequate knowledge of personal finances (Jump$tart Coalition, 1998, 2000, 2002; National Council for Economic Education, 2003). This lack of knowledge was documented by the results of a 1998-99 national test administered by the National Council on Economic Education. Among the 1010 adults and 1085 high school students who were tested, 49% of adults and 66% of high school students received an "F." The average grade was 57% for adults, while the average grade for high school students was 48% (Brenner, 1999). A test of more than 1500 high school students' knowledge of basic personal finance, sponsored by the Jump$tart Coalition for Personal Financial Literacy in 1997, revealed similar results. The average score was 57.3%, with only 10.2% scoring a C or better, while 44.2% failed the exam (Mandell, 1998). According to a press release from the Jump$tart Coalition (1998), the results of the survey indicated that "today's students are likely to become tomorrow's financially strapped adults." Dr. Lewis Mandell, the economist and university professor who conducted the survey, painted a dismal picture of young people in 37 America leaving school without any basic skills in personal finance. He asserted that this puts them at high risk of becoming adults who end up deeply in debt, in bankruptcy, or without adequate savings for their retirement (Jump$tart Coalition, 1998). Subsequent nationwide surveys of 12*'^ graders by the Jump$tart Coalition revealed declining levels of personal financial literacy among high school students. The 2000 results showed that participants answered 51.9% of the survey questions correctly, compared with 57.3% in 1997 (Jump$tart Coalition, 2000). In 2002, on average, participants answered only 50.2% of the questions correctly. The 2002 survey revealed that males averaged slightly higher scores than females, while Caucasian students scored higher than any other ethnic group (53.7%). Other findings revealed that while personal financial literacy is declining, credit card usage is increasing among high school students, with the percentage of students who do not use credit cards down to 67.8% compared with 70.8% in 1997 (Jump$tart Coalition, 2002). Similar results were found among college students. Markovich and DeVaney (1997) found that seniors at a large Midwestern university were not very well informed about credit use, and female students had lower knowledge scores than male students. In a survey of 500 randomly selected seniors, results indicated that students were more satisfied with their financial management skills than with their knowledge. Most students agreed that taking a financial management course would be of benefit. There was a strong correlation 38 between satisfaction with knowledge and seniors' ability to manage their personal finances. Bradshaw and Evers-Lush (1993) surveyed business majors at multiple colleges in the Southern region of the country. Although most students (70%) perceived themselves to be knowledgeable about credit cards, they were willing to pay high interest rates to credit card companies. This is an indication that perceived knowledge was not necessarily reflected in credit card behavior. Only 10.2% of high school students and 32.2% of college students reported that they had learned strategies for wise credit card use in either high school or college. These findings were supported by a 1999 Youth & Money survey, conducted by the American Savings Education Council (ASEC). Only 21% of the students, ages 16 to 22, surveyed reported that they had taken a personal finance course in school, and two-thirds acknowledged they needed to know more about money management. At the same time, students gave positive reports of their financial knowledge and money management abilities, with 67% of participants reporting that they understood financial matters fairly well. However, the researchers pointed out that although these self-appraisals may be "verified by some behavior, they are called into question by other reported attitudes and behavior (ASEC, p. 1). For example, two-thirds (64%) of students stated that they did not know as much about money management as they should, while one-quarter of those who believed they did a good job of managing their money did not consider savings to be a high priority (ASEC). 39 In a survey of personal financial literacy of 924 students from multiple universities across the nation, Chen and VoIpe (1998) found the overall mean of correct answers for the survey to be only 53%. Among the participants, women, non-business majors, students under age 30, students in the lower academic class ranks, and those with little work experience had lower levels of knowledge. The researchers concluded that college students are not knowledgeable about personal finance, and this lack of knowledge will limit their ability to make informed financial decisions. Chen and VoIpe contended that limited knowledge of personal finance by college students may be attributed to a "systematic lack of a sound personal finance education" in the nation's education system (p. 112). The researchers asserted that this lack of education is reflected in the serious financial illiteracy of the American people. In contrast, a study by Upton (1992) among students at Tennessee State University concluded there were no significant differences in knowledge of credit practices based on whether a student completed a consumer education/ economics class. However, results of this study were not generalized to the entire student body of the university. A study by Dickerson (1998), conducted at Bowling Green State University, identified no significant correlation between students' credit card knowledge and credit card behavior. The author argued that sensation-seeking may be a better predictor variable of behavior than knowledge. He suggested that researchers pursue ongoing studies to investigate the "relationship between 40 sensation-seeking as a predictor variable and use of credit cards among college students" (p. 48). In a study by Punjavat (1992), it was found that the relationship between credit card knowledge and credit card experiences was not statistically significant; however, there were significant relationships between credit card knowledge and credit card attitudes, practices, and satisfaction. The respondents were international graduate students at Colorado State University. Many of them had never used credit cards before arriving in the United States and had never had any formal instruction in consumer credit. It was found that when knowledge increased, there was a direct relationship to increased awareness of credit card interest rates and there was increased satisfaction in their use of credit cards. Regarding differences found in credit card knowledge between students from the College of Human Sciences and other colleges at the Florida State University, Brundage (2001) found that Human Science majors scored the highest among college majors. The author also found "variance in scores between participants of different ages and races" (p. 57), where those older than 23 scored higher than their younger counterparts, and whites scored higher than other races. 41 Credit Card Attitudes Consumer attitudes toward debt have changed considerably over the past century. Debt, at one time, was "something to be avoided at all costs" (Ritzer, 1995, p. 6). As the credit card industry developed and became more widespread, Mandell (1972) reported that Americans were far more likely to use credit cards than to approve of them. Today "consumer dependence on credit is an accepted part of everyday life, a signifier in one's 'rite of passage'" (Klein, 1999, p. 1). According to Klein, "consumer credit privileges awarded to a college student... are an indication of credentialed status" (p. 1). Indeed, Klein contended, "receipt of an initial credit account is essentially a form of formal economic status recognition" (p. 1). Nevertheless, Americans continue to have both positive and negative feelings toward credit cards. Studies conducted among college students revealed that students generally have favorable attitudes toward credit cards and credit card debt (Warwick & Mansfield, 2000; Xiao, Noring, & Anderson (1995). Punjavat (1992) found that international graduate students tended to have favorable attitudes toward credit cards. However, attitudes were more favorable if students had used credit cards prior to arriving in the United States and/or if students' parents owned credit cards. American students appeared to be more tolerant of debt than the public in general (Davies & Lea, 1995). Hayhoe, Leach, and Turner (1999) suggested that students have been raised in a time of "easy credit and 42 living beyond one's means" (p. 647), and this is reflected in the fact that students may be more likely to have favorable attitudes toward credit and debt. In a study conducted among students at one university in England, Davies and Lea (1995) concluded that students were a "relatively low-income, high-debt group with relatively tolerant attitudes towards debt" (p. 664). Findings of the study showed that greater tolerance of debt (and higher debt levels) was associated with being at the university longer. The researchers suggested that increase in debt occurred prior to the increase in debt tolerance and this may be explained by the fact that students adjust their attitudes to match their debt level. Students were from relatively high-income families, and in an attempt to maintain their accustomed lifestyle while temporarily experiencing low incomes, they were willing to tolerate some level of debt. Xiao et al. (1995) developed an instrument for measuring college students' attitudes toward credit cards, based on a tripartite classification of attitude- affective, cognitive, and behavioral. Overall, students surveyed, at an eastern university in the United States, displayed favorable attitudes toward credit cards. However, students showed most favorable attitudes in the affective component (82%), more favorable in the cognitive component (67%), and least favorable in the behavioral component (20%). Gender, college major, living arrangements, and number of working hours affected student credit card attitudes. Among those surveyed, females majoring in consumer affairs, living on campus, and/or 43 working less than 20 hours a week were more favorable toward credit cards (Xiaoetal., 1995). Utilizing the Xiao et al. (1995) rating scale, with its affective, cognitive and behavioral classifications, as well as a money attitudes scale, Hayhoe et al. (1999) examined credit card use by students at five state-sponsored universities in the United States. Of the 426 students surveyed, 80% owned at least one credit card. Findings showed that students possessing four or more credit cards scored higher on the affective credit attitude than did students with one to three cards. This supported the conclusions of Xiao at al. Participants scoring higher on the affective credit attitude also were more likely to be older and female. In contrast, a study by Joo et al. (2003) of 242 students in the College of Human Sciences at a large university in the southwest did not find age and gender to be significantly related to college students' attitude toward credit cards. The findings also differed from those of Xiao et al. in that academic major, employment status, and living arrangements were not significant facrtors affecting college students' credit attitudes. Joo et al. found that ethnicity, credit card ownership, and academic level were significant factors affecting students' attitudes toward credit. Whites and those who possessed credit cards had more positive attitudes toward credit, while those who were in higher academic years had negative attitudes toward credit. In addition, background characteristics (parents' credit use and credit problems) and psychological factors (money ethic and locus of control) were significantly related to students' credit attitudes. 44 students whose parents used credit cards often had a positive attitude toward credit, while those whose parents had experienced credit-related problems had a negative attitude toward credit cards. Students who had a positive money ethic and those with a strong external locus of control had positive attitudes toward credit. Credit Card Practices Credit card practices may include reasons for owning credit cards, number of credit cards owned, types of credit card purchases, frequency of usage, payment practices, and other factors associated with owning and using credit cards. Several studies have examined college students and credit card practices. Manning (1999) reported that socioeconomic background has a strong influence on students' receptivity to having their own credit cards. He pointed out that colleges whose student body came mainly from middle and upper income families, with college educated parents, will have a greater number of students owning credit cards. For example, approximately 70% of undergraduate students at four-year institutions own at least one major credit card. However, at Georgetown University where students' median household income is $155,000, over four out of five students have their own credit cards (Manning). A study by Warwick and Mansfield (2000), conducted at a small, private university in the Midwest, disclosed that among students with at least one credit card, only 15% had requested an application directly from the credit card issuer. 45 Another 37% of the students had received an unsolicited application in the mail, while 33.6% had received the application on campus. Applications were obtained at kiosks during special campus events and with textbook purchases from the school bookstore. The authors pointed out that the majority of college students who use credit cards do not actively seek them out. Instead, credit card companies aggressively pursue students through the mail and on campus (Wanwick & Mansfield). College students who own credit cards are typically full- time undergraduates who are usually employed in part-time minimum wage jobs. Approximately two-thirds of the 381 students surveyed by Wanwick and Mansfield owned at least one card. Of those possessing cards, 22.8% owned one, 20% owned two, and almost 4% owned over five cards. Pinto, Parente, and Palmer (2001a) examined the relationship between credit card usage, employment, and academic performance among a group of students at three college campuses in the Northeast. The researchers found a significant relationship between employment and credit card usage (number of cards and total balance). However, there was no significant relationship between employment and credit card usage and academic performance, neither was there significance between academic performance and number of credit cards possessed or balance owing. Pinto et al. postulated that although some students did perceive the need to work additional hours to pay for their credit cards, this did not have a significantly adverse effect on their academic performance. 46 Armstrong and Craven (1993) surveyed 243 students from a large, Midwestern state-university on their credit card usage and payment practices. Findings revealed that most students reported owning at least one credit card, and the number of cards per person ranged from 1 to 25. Among credit card holders, 29% owned one card, 38% of the students owned two or three cards, and 31% possessed four to eight cards. Approximately 4% reported owning 10 to 25 cards. Gender and race were predicators of number of cards owned, with white female students reporting the greatest number of cards. Although students reported that the top reason for using credit c:ards was convenience, results showed they were actually revolving credit users, spreading payments over time, and paying interest charges on monthly balances. In addition, over 40% of the students did not know the current interest rate on the card they used most. According to Armstrong and Craven, approximately one-third of the respondents showed signs of needing financial counseling. Hayhoe, Leach, Turner, Bruin, and Lawrence (2000) examined the impact of gender on types of credit card purchases and found significant gender differences. The study was conducted among 208 male college students and 272 female college students from six state-sponsored universities. The survey revealed that female students used their credit cards more than male students to purchase clothes, while male students used their cards more for electronics, entertainment, and food away from home. In addition, gender was significant in the financial practices of college students. Female students were more likely to 47 report that they kept a written budget, used a shopping list, kept bills and receipts, planned their spending, and saved regularly. However, there were no significant differences between males and females on the practice of paying interest, making minimum payments, and feeling they did a good job managing their finances. In their study on undergraduate students at a southeastern university in the United States, Jamba-Joyner, Howard-Hamilton, and Mamarchev (2000) reported that 74% of the participants owned at least one credit card. Ownership of credit cards did not significantly differ by gender, race, financial aid, academic level, socioeconomic status and area of study. This finding confirms the claim by Manning (2000) that the "credit card industry's penetration of the college student market has been extremely successful" (p. 6). Jamba-Joyner et al. found that women and students of color were most likely to carry forward a credit card balance every month. In addition, students from a lower socioeconomic background and those who possessed a large number of cards were most likely to maintain a credit card balance. The researchers pointed out that these students already face challenges because of their background; credit card debt adds an additional burden. A national computer-assisted telephone survey of college students conducted by The Education Resources Institute (TERI) and the Institute for Higher Education (IHEP) (1998) revealed that students have "reasonable attitudes about how credit cards should be used" (p. 9). When asked to rate the 48 importance of specific reasons for using credit cards, 52% of the 750 participants gave "high importance" to using a credit card to build a credit history. The next highest percentage rating (45%) was using credit cards for emergency purposes. Of the students surveyed, 44% reported using credit cards for living expenses, 24% for large, occasional purchases, and 22% for education-related expenses. Most of the respondents indicated that they would prefer not to use credit cards for education-related expenses. However, one in five students reported that they had used credit cards to pay for tuition and fees at some time, with 43% of those indicating that they had not paid off the balance immediately. Because another 57% of the respondents had paid for books and supplies with credit cards during that current year, the researchers concluded that a substantial proportion of students' credit card charges consist of education-related expenses. While most students charged because of convenience and paid off the balances each month, there were some students who carried over their balances and paid interest, thereby increasing the cost of their college education. Credit Card Debt Reports on college students' credit card behavior, with regard to students' debt, have been mixed. Some researchers and industry representatives have reported that most students generally act responsibly by paying off their credit card balances each month (Jamba-Joyner et al., 2000; Murdy & Rush, 1995; 49 TERI/IHEP, 1998). Others have painted a more alarming picture of the state of college students and credit card debt (GAO, 2001; Manning, 2000). In their study of credit card use and payment practices of college students, Armstrong and Craven (1993) indicated that 27% of the respondents reported paying their balance in full. Of the students surveyed, 15% had debts under $200, 25% reported balances between $201 and $600, and 15% had debts between $601 and $1,000. Another 10% had balances between $1001 and $1,600, and 6% had balances up to $1,800. Contrary to the findings of a study by the Bank Card Observer in 1987 (Armstrong & Craven), which reported women having higher balances on bank cards than men, Armstrong and Craven's study showed no significant difference in gender and outstanding credit card balances. Both studies differed from the Davies and Lea study (1995) which reported higher levels of debt among male students than among female students. In a two-part study conducted among college students from six universities in spring 1997 and 1999, Hayhoe (2002) reported that in addition to credit card debt, students also carried a variety of other debts. Results from the first survey showed that 12% of students had home mortgages, 1% a home equity loan, 21% car loans, 50% student loans, 5% outstanding medical bills, and 8% loans from family and friends. The second study, conducted two years later, revealed an increase in the number of students with debts, with 19% of students having home mortgages, 2% home equity loans, 38% car loans, 63% student 50 loans, 9% outstanding medical bills, and 10% loans from family and friends. In addition, outstanding credit card debts ranged from less than $50 to more than $5,000, with the mean being $500 to $750 (Hayhoe, 2002). A national survey, conducted by TERI/IHEP (1998) reported that the majority of students use credit cards responsibly and do not carry large amounts of credit card debt. The TERI/IHEP study found that 59% of students pay off their monthly balances right away, and of the 41% who carry over monthly balances, 81% pay more than the minimum amount due. Of the students reporting balances, 82% reported having an average balance of $1,000 or less, while 9% reported an average balance of between $1,001 and $2,000 (TERI/IHEP). Similar results were recorded by a Student Monitor study, conducted in 2000 among students from 100 campuses across the country, where the average monthly balance of the 42% who carried over a balance each month was $577. Only 16% of those carrying a monthly balance reported a balance of more than $1,000 (GAO, 2001). The results of these two studies differ significantly from that of a Nellie Mae study, conducted in 2000, which reported that the average credit card balance for undergraduate students was $2,748, with 9% having balances exceeding $7,000 (Nellie Mae, 2002). A similar study by Nellie Mae in 1997 showed students leaving college with an average student loan debt of $12,000, in addition to more than $2,000 in credit card debts (Blair, 1997). According to Blair, this presents an alarming picture of young people who are overextending 51 themselves with credit card and student loan debt during their college years and who may be "setting themselves up for failure when they graduate" (p. 3). The differences in results among these studies may be explained, in part, by sample selection. Whereas the TERI/IHEP and Student Monitor studies utilized a random sample from among a broader population of college students, the Nellie Mae sample came from a small number of students applying for a loan from Nellie Mae. In addition, bias may account for the differences, as the TERI/IHEP and Student Monitor studies relied on self reporting and may have been subject to the limitations of non-response and reliance on memory (GAO, 2001; Palmer etal., 2001). Research conducted by Robert Manning, among students at Georgetown University, American University, and the University of Maryland in 1999, suggested that earlier researchers had underestimated the level of debt among college students. According to a press release by Consumer Federation of America (1999), students who maintained balances on their credit cards averaged more than $2,000, with one-fifth carrying balances of more than $10,000. However, since students often refinance their credit card debt with student loans or debt consolidation loans, a more accurate total debt level for a typical college senior is $20,000 (CFA, 1999; Palmer et al., 2001). 52 Summary A revised model of the Deacon and Firebaugh (1988) individual personal and managerial system, with its input, throughput, and output components, provided the conceptual framework for the study. The Input component is college students' socio-demographic characteristics. Throughput consists of college students' credit card knowledge and credit card attitudes, and the output component is students' credit card practices. Since the origin of credits cards in the early 1900s in the United States, when hotels, department stores, and oil companies issued cards to their customers, the credit industry has grown rapidly. The period following World War II saw the rise of the modern, universal credit card and the involvement of banks in the credit card industry. By 1979, in excess of 11,000 banks had become members of the now popular Visa and MasterCard networks. Rapid growth continued through the use of aggressive marketing techniques, with the current trend being to target college and high school students as a source of new credit card owners. The literature on this latest phenomenon revealed that American young people are inadequately prepared to handle credit cards. Their lack of knowledge of basic personal finance puts them at risk of becoming deeply in debt. This is especially alarming as research showed college students generally have favorable attitudes toward debt and that most college students own at least one credit card. Although the literature reported varying levels of students' credit 53 card debt, studies showed that some students' credit card behavior could lead to higher debt levels and misuse. Variables such as academic performance, gender, and socioeconomic background reflected differences among students' credit card knowledge, attitudes, and practices. 54 CHAPTER III METHODS AND PROCEDURES This chapter provides a description of the research methods and procedures that were utilized in the study. It focuses on the research design, the development of the survey instrument, selection and description of the sample, data collection, and data analysis. Research Design In this study, the researcher sought to provide data on the credit card behavior of students enrolled at TTU. Specifically, the study examined the interrelationships between students' socio-demographic background (independent variable), students' credit card knowledge and credit card attitudes (mediating variables), and students' credit card practices (dependent variable). The study was designed to address the following questions: 1. How did college students acquire their credit cards? 2. What reasons do students give for using credit cards? 3. What types of purchases do students make most frequently with credit cards? 4. Where do college students obtain their knowledge of personal finance/money management principles? 55 5. What are college students' attitudes toward financial education and counseling? 6. What is the debt level of college students as it relates to: a. credit card debt b. student loan debt c. other consumer debt? 7. What is the relationship between selected socio-demographic variables (age, gender, ethnicity, marital status, classification, major, grade point average, employment, income, sources of income, and socioeconomic background) and a. college students' credit card knowledge b. college students' credit card attitudes? 8. What is the relationship between selected socio-demographic variables (age, gender, ethnicity, marital status, classification, major, grade point average, employment, income, sources of income, and socioeconomic background) and college students' credit card practices (reasons for using credit cards, number of cards, types of purchases, repayment practices)? 9. What is the relationship between: a. college students' credit card knowledge b. college students' credit card attitudes 56 and college students' credit card practices (reasons for using credit cards, number of cards, types of purchases, repayment practices)? Design and Development of the Instrument The survey instrument used for data collection in this study was developed by the researcher after a literature search yielded no existing single instrument suitable for the study. A review of questionnaires used in previous studies on college students and credit cards (Bradshaw & Evers-Lush, 1993; Joo & Grable, 1999; Markovich & DeVaney, 1997; Pinto et al., 2000; Sholten, 1981; Tan, 1993; Xiao et al, 1995), as well as personal communication with researchers, generated concepts and questions for the development of an instrument. A tentative questionnaire was developed, consisting of 39 questions requiring a variety of responses, including true/false, fill in the blank, and rating on a Likert- type scale. Permission was received from So-hyun Joo (Joo & Grable, 1999), Mary Beth Pinto (Pinto et al., 2000), and Jing J. Xiao (Xiao et al., 1995) to use items from their studies in the new questionnaire. The instrument was submitted to the dissertation committee for evaluation, and revisions were made based on the committee's recommendations. To establish face and content validity, the instrument was mailed to 15 researchers and educators across the country for their review. Feedback was received from nine reviewers, who made suggestions regarding content, format of questions, and appearance. Based on their recommendations further modifications were 57 made to the instrument before it was resubmitted to the dissertation committee. Following the approval of the committee, a pilot study was conducted at the beginning of the fall 2003 semester at TTU among 79 students. Thirty students were entering freshmen enrolled in an orientation course, and 49 were juniors and seniors enrolled in a capstone course in the College of Human Sciences. Following the pilot test, minor modifications were made to the wording of some items to make them less open to misinterpretation, while ranges were revised for the items measuring income and debt levels. Reliability coefficients for internal consistency were determined for the two attitude scales (discussed later in this chapter), which resulted in two items being dropped and others reworded. The alpha levels for the attitude scales were .75 and .65. In addition, modifications were made to the general appearance of the questionnaire, and a cover was designed, before the questionnaire was printed and distributed. The 12-page questionnaire. College Student Credit Card Survey (Appendix A), included items designed to measure college students' credit card knowledge, attitudes, practices, and socio-demographic characteristics. The questionnaire items designed to measure credit card knowledge were adapted from Pinto et al. (2000). The knowledge scale consisted of 17 true/false statements related to the costs of credit, credit history, repayment practices, and legal regulations. A knowledge score was calculated for each student. Three other questions were included in the questionnaire to determine students' 58 perception of their level of financial knowledge and their sources of that knowledge. Two questions, one with 11 items and the other with 13 items, were included to measure college students' attitudes. Question 14 in the questionnaire is a modified version of the credit attitudes scale developed by Pinto et al. (2000), which measured how students view and use credit cards. It was designed to be answered by only those students who had at least one credit card in his/her name. Question 16 in the questionnaire was a modified version of an attitude scale developed by Xiao et al. (1995). Four statements measuring college students' attitudes toward credit counseling and education were included in this question. However, data analysis revealed that these four statements decreased the reliability coefficient of the scale. Therefore, they were removed from the attitude scale and analyzed separately. Sixteen questions in five categories measured college students' credit card practices. The categories consisted of the number and types of cards possessed, period and influences of card acquisition, usage patterns of credit card holders (reasons for using cards, types and frequency of purchases, monthly amount charged, total credit limit and annual percentage rates, and number/purposes of cash advances), repayment practices, and debt level. The question relating to influences on college students' acquisition of credit cards consisted of 11 items on a four-point Likert scale, ranging from Extremely 59 Influential to Not at all Influential. Types and frequency of purchases were measured by 12 items in a 4-point Likert scale with anchors of Often and Never, while reasons why college students use credit cards was measured by 11 items in a 4-point Likert scale, ranging from Very Important to Not at all Important. Questions were included to obtain the following socio-demographic characteristics: age, gender, ethnicity, marital status, college classification (year), major, grade point average, number of hours employed, students' total annual income, sources of students' income, and socioeconomic background. Students' socioeconomic background was determined by parents' total annual income, parents' highest level of education, and parents' credit experiences. In addition, two questions were included in the questionnaire to reveal whether TTU students were aware of the Red to Black program and have utilized its services. Selection and Description of the Sample The sample for this study was drawn from the TTU student population enrolled during the fall 2003 semester. Using the TTU Schedule of Classes Web page, the researcher identified the names of the colleges and the various departments within each college. The names of over 200 professors/instructors teaching undergraduate courses in various departments were purposefully selected as possible contacts, and a letter introducing the study and asking for instructors' assistance was prepared by the researcher (Appendix B). 60 Over a four-week period, beginning on October 6, 2003, the researcher visited departments in nine colleges to make personal contact with instructors. In addition to visiting instructors in their offices, the researcher also left letters introducing the study in instructors' mailboxes with telephone and e-mail contact information. Thirty-nine instructors agreed to allow their students to complete the questionnaires during twenty to twenty-five minutes of class time. Because of the ovenvhelmingly positive response from some colleges/departments and the negative or poor response from others, an attempt was made by the researcher to include some general education courses consisting of students from several different majors. As a result, the sample included students from all nine colleges. In addition, the researcher deliberately targeted instructors of sophomore, junior, and senior level classes. Since the study was being conducted in the middle of the fall semester, it was assumed that freshmen courses would yield large numbers of students who had not yet acquired and/or used credit cards. It was assumed also that a large number of freshmen would be among the students enrolled in some sophomore-level courses and would, therefore, be included in the sample. The survey was completed by a convenience sample of 2,154 freshmen, sophomores, juniors, and seniors in 50 different class sections. Of the completed questionnaires, 2,113 were deemed usable for the study. 61 Data Collection A brief summary of the research proposal and the instrument, including a letter of introduction, were submitted to the Texas Tech University Committee for the Protection of Human Subjects for approval. Following this committee's approval, contacts were made with instructors and appointments set up to deliver or administer the questionnaires. Instructors who granted permission for their class to participate in the study were given the choice of administering the questionnaires themselves, at their own convenience, or of having the researcher carry out this responsibility at an agreed upon time. Many of the instructors preferred the researcher to administer the survey, and for this reason it was necessary to spread the data collection over a seven-week period during October and November 2003. Students in the classes were offered an opportunity to participate in a drawing for a cash prize of $100 by filling out a slip with their name and contact information. The slips were separated from the questionnaires, which were completed anonymously. Students were asked not to complete the questionnaire if they had completed it in another class. Data Analysis Data were analyzed using the Statistical Package for Social Sciences (SPSS) and Analysis of Moment Structures (AMOS) to answer the research questions identified at the beginning of this chapter. Descriptive statistics including frequencies, means, and percentages were utilized when appropriate to 62 describe responses to the independent variables (socio-demographic characteristics); the mediating variables (credit card knowledge and credit card attitudes); and the dependent variable (credit card practices). In addition, descriptive statistics were applied to determine how participants acquired credit cards, their reasons for using credit cards, and the types of purchases made. Descriptive statistics also identified where college students had obtained their knowledge of personal finance/money management principles; college students' attitudes toward financial education and counseling; and college students' credit card, student loan, and other consumer debt. College students' credit card knowledge was measured by questionnaire items adapted from Pinto et al. (2000). The knowledge scale consisted of true/false statements related to the costs of credit, credit history, repayment practices, and legal regulations. A knowledge score was calculated for each student. Items were receded so that students received a score of 1 for each true response and 0 for each incorrect response, with 17 being the maximum score possible. College students' attitudes toward credit cards were measured by two attitude scales. The first was a modified version of an attitude scale developed by Pinto et al. (2000), which measured the way college students use and view credit cards. It consisted of 11 statements on a four-point Likert scale, with choices ranging from Strongly Agree to Strongly Disagree. The question was intended only for those students with at least one credit card in their name. An 63 index of college students' attitudes toward the use of credit cards was computed for this question, and possible scores ranged from 11 to 44. Those students who had higher scores on the use of credit cards attitude scale were assumed to have a less healthy attitude toward credit cards. A second question consisted of nine statements, eight of which were modified items taken from a study by Xiao et al. (1995) on favorable and unfavorable affective, cognitive, and behavioral attitudes toward credit cards. The items were rated on a four-point Likert scale, with choices ranging from Strongly Agree to Strongly Disagree. This question was designed to be completed by all students, whether or not they had acquired a credit card in their name. Items worded negatively were reverse coded, and an index of attitudes toward acquisition and use of credit cards was computed for this question. Possible scores ranged from 9 to 36. Students who had higher scores on the acquisition and use of credit cards attitude scale were assumed to have a more positive attitude toward credit cards. Four additional statements had been included in this questionnaire item to measure college students' attitudes toward credit counseling and education. However, data analysis revealed that these four statements decreased the reliability coefficient of the scale, and they were removed from the attitude scale and analyzed separately using descriptive statistics. Stepwise multiple regression analyses were used to identify the relationship between socio-demographic variables and college students' credit 64 card knowledge and attitudes (Input to Throughput variables) as well as credit card practices (Input to Output variables). Several variables were dummy-coded for the regression analysis. A description of the variables is shown in Table 3.1. Table 3.1. Definition of Variables Used in Multiple Regression Analyses. Variables Definition Age Continuous variable Gender 0 = male, 1 = female Ethnicity 1 = white, 0 = all others Marital status 1 = never married, 0 = all others College classification Dummy Variable Entering freshman 1 = Entering freshman, 0 = all others Freshman 1 = Freshman, 0 = all others Sophomore 1 = Sophomore, 0 = all others Junior 1 = Junior, 0 = all others Senior 1 = Senior, 0 = all others Academic Major Dummy Variable Agricultural Sciences 1 = Agricultural Sciences, 0 = all others Arts & Science/Visual & Performing Arts/ 1 = Arts & Science/Visual & Perfonning Arts/ Education Education, 0 = all others Business 1 = Business, 0 = all others Engineering/Architecture 1 = Engineering/Architecture, 0 = all others Human Sciences 1 = Human Sciences, 0 = all others Grade Point Average Continuous variable, 1.00 to 4.00 Hours of employment Interval Level. Possible range 0 to >40 Students' income 11 levels: < $3,000 to >$30,000 Sources of income 1 = yes, 0 = no Parents' total annual income before taxes 5 levels: Less than $25,000 to >$ 100,000 Father's highest level of education 7 levels: Did not complete high school to doctorate 65 Table 3.1. Continued. Variables Definition Mother's highest level of education 7 levels: Did not complete high school to doctorate Parents' use of credit 4 = very often, 3 = often, 2 = occasionally, 1 = never Parents' problems with credit 4 = very often, 3 = often, 2 = occasionally, 1 = never Knowledge 0 = false, 1 = true. Possible score 1-17 Attitudes Two composite scores were computed using responses from statements found in two questions on attitudes toward credit cards. Number of cards held by student Number of credit cards, store cards and gas cards were summed to form one continuous variable Times late paying cards in last 6 months Continuous: 0 to >2 Purchases made by college students 4 = Often, 3 = Sometimes, 2 = Hardly Ever, 1 = Never. Reasons for using credit cards 4 = Very important, 3 = Important, 2 = Somewhat important, 1 = Not at all important. Stepwise multiple regression analyses also were applied to establish relationships between college students' credit card knowledge and attitudes and college students' credit card practices (Throughput to Output variables). To reduce the number of variables measuring why college students use credit cards and the types of items purchased, factor analyses were used to find clusters of related variables (see Tables 3.2 and 3.3). An alpha level of .05 was used to 66 determine statistical significance, and cases were excluded on a painwise basis. This means that when subjects have missing variables, their data were excluded only for the calculations involving the missing variable. Table 3.2. Factor Loadings for Questionnaire Item #13~Reasons Why College Students Use Credit Cards. Scale Items Convenience/ Support for Emergency/ Incentives Lifestyle/ Credit Record Education Credit cards used for convenience .747 Credit cards used for safety .639 Credit cards used to make returning .593 merchandise easier Credit cards used for the incentives .560 Credit cards used to help me make ends .774 meet because 1 often run out of money Credit cards used to borrow for my .586 college education Credit cards used to keep up with my .571 friends. Credit cards used to get cash advances .544 Credit cards used to buy a .512 product/service immediately and pay it off later Credit cards used to cover emergency .758 needs Credit cards used to establish a credit .529 record Percentage of Total Variance 23.77 14.61 9.18 Explained 67 Table 3.3. Factor Loadings for Questionnaire Item #7~Types of Credit Card Purchase Items. Scale Items Food/Clothing/ Education/ Housing/Cash Leisure Emergency Advances Credit cards used for eating out .847 Credit cards used for entertainment/ .817 sports Credit cards used for clothing .685 Credit cards used for groceries/ .666 household items Credit cards used for vacation/travel .564 expenses Credit cards used for tuition/fees .825 Credit cards used for textbooks/ school .776 supplies Credit cards used for emergencies .542 Credit cards used for rent/mortgage .718 Credit cards used for utility/phone bills .673 Credit cards used for cash advances .627 Percentage of Total Variance 33.63 11.52 11.21 Explained In addition, a path analysis methodology was used to test the conceptual model with its hypothesized relationships between variables, as represented in Figure 3.1. Because of the large number of input and output variables, identified in Research Questions 7-9, several variables needed to be deleted before the path analysis could be conducted. Based on the literature, as well as on their fitting the criteria of continuous or dummy coded variables, age, gender, college classification, and grade point average were included in the path analysis as input variables. Usage and total debt were included as output variables. 68 0) O) ro S (0 •5 0) .0 l-Q Us a Q. ra1— O) 0 E 0) Q 0 0 0 CO (/) 0) J3 ra ^ ra > c 0 a> J <1> ize d (0 0 sz ra •*-•0 Q^. >sT3 X ra x: C) ^ TJ pt u 0) (1) T3 (1 3 r +^ 0 tS 0 < <1) CO O) CO •0 <1) 0) ^ 3 0 U5 c U- ^<:: 69 An index of usage scores was computed for question 7 on the questionnaire, with possible scores ranging from 12 to 48. The item measured how often students paid for certain selected goods and services, such as groceries and clothing, using their credit cards. Students responded to 12 items on a four-point Likert scale, with choices ranging from Often to Never. Total debt was computed by summing students' responses to Question 2.Id, 2.2d, and 2.3d, which asked respondents to fill in their credit card, store card, and gas card balances. The path analysis methodology was selected because it allowed the researcher to investigate relationships among variables. Goodness of fit indexes, such as normed fit index (NFI), root mean square error of approximation (RMSEA), comparative fit index (CFI), and the Bentler-Bonnet non-normed fit index (NNFI) were used to determine whether the model was supported by the empirical data. Summary This chapter provided a description of the research methods and procedures used during the study. The study examined interrelationships between students' socio-demographic characteristics (independent variable), students' credit card knowledge and credit card attitudes (mediating variables), and students' credit card practices (dependent variable). 70 The instrument used for data collection in the study was designed by the researcher to measure college students' knowledge, attitudes, and practices and to provide socio-demographic characteristics of the participants. The questionnaire was reviewed by a panel of experts to establish face and content validity. A pilot study was conducted and minor changes made before the instrument was administered to a convenience sample of 2,154 freshmen, sophomores, juniors, and seniors enrolled in 50 class sections at TTU. Of the completed questionnaires, 2,113 were used in the study. Data for the study were collected over a seven-week period in the fall 2003 semester during 25 minutes of class time. The survey was administered either by the researcher or by the class instructor at a time convenient to the class instructor, and students were instructed not to complete the survey if they had completed it in another class. Data were analyzed using SPSS and AMOS. Descriptive statistics and stepwise multiple regression analyses were conducted to answer the research questions, and a path analysis methodology was used to test the conceptual model and to investigate relationships among variables. Goodness of fit indexes, such as NFI, RMSEA, CFI, and NNFI were used to determine whether the model was supported by the empirical data. 71 CHAPTER IV ANALYSIS AND INTERPRETATION OF DATA Data for the study were used to describe students' credit card knowledge, attitudes, and practices. The results of the analyses of data are presented in the following sections: (a) demographic characteristics of the sample (descriptive statistics of the input variables), (b) descriptive statistics of the throughput variables, (c) descriptive statistics of the output variable, (d) results related to the research questions, and (e) results from the statistical testing of the conceptual model. Characteristics of the Sample (Input Variables) Descriptive statistics were used to describe the characteristics of the sample, which consisted of 2,113 undergraduate students enrolled at TTU in the fall 2003 semester. Socio-demographic and background data are reported in Table 4.1. Socio-demographic variables analyzed in the study were gender, age, marital status, ethnicity, employment, classification, major (college), and grade point average. Table 4.2 provides a summary of students' socioeconomic and family background variables, which included students' annual income and sources of income, annual household income of parents, fathers' and mothers' highest level of education, and parents' experiences with credit cards. 72 Table 4.1. Socio-Demographic Characteristics of the Sample. Variables Categories n % N Gender Male 1013 48.3 2097 Female 1084 51.7 Age 17-20 947 45.2 2096 21-24 1015 48.4 Over 24 134 6.4 Marital status Never married 1893 90.3 2096 Not married, but partner 69 3.3 Married 112 5.3 Separated/Divorced/Widowed 22 1.0 Ethnicity White 1794 85.8 2091 Black 53 2.5 Hispanic 176 8.4 American Indian/Alaskan 4 0.2 Asian 20 1.0 Hawaiian/Pacific Islander 2 0.1 Other race 42 2.0 Hours employed 0 814 38.9 2095 10 or less 186 8.9 11-20 482 23.0 21-30 395 18.9 31-40 173 8.3 more than 40 45 2.1 College classification Entering Freshman 167 8.0 2089 Freshman 135 6.5 Sophomore 311 14.9 Junior 688 32.9 Senior 788 37.7 Majors Agricultural Sciences 168 8.0 2097 Arts&Sc/Vis&Perf Arts/Education 492 23.5 Business 642 30.6 Engineering/Architecture 192 9.2 Human Sciences 603 28.8 GPA 1.00-2.49 95 5.1 1858 2.50-3.49 1215 65.4 3.50-4.00 548 29.5 73 Table 4.2. Socioeconomic Background of the Sample. Variables Categories n % N Students' total income Less than $3,000 546 27.2 2007 $3,000-$5,999 417 20.8 $6,000-$8,999 262 13.1 $9,000-$11,999 211 10.5 $12,000-$14,999 194 9.7 $15,000-$17,999 112 5.6 $18,000-$20,999 70 3.5 $21,000-$23,999 45 2.2 $24,000-$26,999 52 2.6 $27,000-$29,999 17 0.8 30,000 or more 81 4.0 Source of financial support Job 1488 71.9 2070 Savings 765 37.0 Spouse 144 7.0 Gifts 643 31.1 Parents 1613 77.9 Interest 161 7.8 Scholarships/Grants 767 37.1 Inheritance 67 3.2 Financial aid 724 35.0 Credit cards/Cash advances 280 13.5 Other sources 44 2.1 Annual income of parents less than $25,000 72 3.5 2085 $25,000-$49,999 236 11.3 $50,000-$74,999 344 16.5 $75,000-$99,999 363 17.4 $100,000 or more 792 38.0 Do not know 278 13.3 Father's level of education Did not complete high school 81 3.9 2083 Completed high school 257 12.3 Some college 484 23.2 Bachelor's 708 34.0 Masters 294 14.1 Professional degree 120 5.8 Doctorate 111 5.3 Do not know 28 1.3 74 Table 4.2. Continued. Variables Categories n % N Mother's level of education Did not complete high school 77 3.7 2084 Completed high school 327 15.7 Some college 619 29.7 Bachelor's 667 32.0 Masters 276 13.2 Professional degree 74 3.6 Doctorate 30 1.4 Do not know 14 0.7 Parents' use of credit cards Never 77 3.7 2106 ' Occasionally 709 33.7 Often 646 30.7 Very often 625 29.7 Not sure 49 2.3 Parents' credit related problems Never 1168 55.5 2105 Occasionally 500 23.8 Often 161 7.6 Very often 123 5.8 Not sure 153 7.3 As shown in Table 4.1, the sample was almost evenly divided between male (48.3%) and female (51.7%). Respondents' ages ranged from 17 to 61, with a mean age of 21. Age groups consisted of 45.2% in the 17-20 year old category, 48.4% in the 21-24 year old category, and 6.4% in the 24 and older category. The majority of the respondents (90.3%) indicated that they had never been married. Students in the sample were mainly white (85.8%) with the next largest group being Hispanics (8.4%). 75 Approximately 39% of the respondents reported that they were unemployed. Of the total sample, 8.9% indicated that they worked less than 11 hours a week. Approximately half the participants were employed between 11 and 40 hours a week, while 2.1% worked more than 40 hours a week. Although no freshman courses were included in the survey, entering freshmen and freshmen enrolled in higher level courses accounted for 14.5% of the respondents. Sophomores totaled 14.9% of the sample, while 32.9% of the respondents were juniors and 37.7% were seniors. Ninety-seven different majors were identified by the respondents. Because some of the cell frequencies were less than three, the majors were receded into eight colleges. The Honors College which consists of students from a wide range of majors was not used in the receding. However, there was representation from all nine colleges since 22 students in a History Honors section completed the survey. Also, it is likely that students completing the questionnaire in other courses may have been enrolled in the Honors College. To further aid in the data analysis, the eight colleges represented were receded into five groups consisting of Agricultural Sciences (8%), Arts & SciencesA/isual & Performing Arts/Education (23.5%), Business (30.6%), Engineering/Architecture (9.2%), and Human Sciences (28.8%). The vast majority of the students (94.9%) reported that they had a grade point average of more than 2.49, with 65.4% reporting a grade point average of between 2.50 and 3.49 and 29.5% reporting a grade point average of between 3.5 and 4.0. 76 As may be expected of college students, respondents' annual income levels were low, with 48% reportedly making less than $6,000 and 23.6% making between $6,000 and $11,999 (see Table 4.2). Another 24.4% of the respondents reported an annual income of between $12,000 and $29,999 with 4% reporting $30,000 and above. Students were asked to indicate all their sources of income. Financial support from parents (77.9%) and student employment (71.9%) were the two leading sources of income. Other sources (2.1%) and inheritance (3.2%) were cited as providing the least amount of financial support. Respondents reporting that credit cards/cash advances accounted for part of their income totaled 13.5% or 280 students. The annual incomes of students' parents were in the upper range with 38% of the students reporting that their parents' annual incomes before taxes were $100,000 or more and 17.4% reporting annual incomes of $75,000 to $99,999. Respondents who reported that their parents' annual incomes were between $50,000 and $74,999 totaled 16.5%. Only 3.5% reported annual household incomes of less than $25,000. Students' socioeconomic backgrounds also were measured by the highest educational level reached by their parents. Students reported that approximately 80% of mothers and 82% of fathers had reached a level of education above that of a high school diploma. Among these, 32% of mothers and 34% of fathers had received a bachelor's degree, while 18.2% of mothers and 25.2% of fathers had received an advanced or professional degree. 77 As recommended in the literature, parents' credit card use and experiences also were used as a measure of family background (Joo et al., 2003). Students were asked to respond to questions relating to parents' experiences, with choices ranging from Never (1) to Very Often (4). A fifth choice (Not Sure or Do Not Know) was included. To the question, "With what frequency did your parents or guardians use credit cards while you were growing up?" 3.7% of the respondents said their parents never used credit cards and 2.3% were not sure. The majority (60.4%) answered that their parents used credit cards often or very often. Students also were questioned concerning whether their parents had experienced any credit-related problems. More than half of the students (55.5%) indicated that their parents had never experienced credit related problems, while 23.8% reported that their parents had experienced problems with credit cards occasionally. Fourteen percent answered that their parents had experienced credit-related problems often or very often. Descriptive Statistics of the Throughput Variables Knowledge Students had a generally positive perception of their level of financial knowledge. More than 90% of the respondents indicated that they were moderately knowledgeable, knowledgeable, or very knowledgeable of personal finance. Only 5.4% believed that they were not knowledgeable (Table 4.3). 78 Question 17 on the questionnaire was used to measure students' knowledge of credit cards. Items were receded with True = 1 and False = 0. A summated index was created to produce the students' knowledge scores to be used in the analyses, and students with less than 50% were considered not knowledgeable. The scores were receded as follows: 1-8 = Not Knowledgeable (less than 50%), 9-12 = Moderately Knowledgeable, 13-15 = Knowledgeable, and 16-17 = Very Knowledgeable. Students' mean score was 12.49. The knowledge scores of 171 students (8.1%) placed them in the Very Knowledgeable category, while 157 students (7.5%) were in the Not Knowledgeable category. When compared with students' perceived level of knowledge, the data indicated that some students may perceive themselves to be more knowledgeable than they really are. However, differences between perceived and actual knowledge were not statistically significant. Table 4.3. College Students' Level of Credit Card Knowledge. Variables n % N Perceived Level of Knowledge Not knowledgeable 114 5.4 2105 Moderately knowledgeable 881 41.9 Knowledgeable 909 43.2 Very knowledgeable 201 9.5 Knowledge Scores Not knowledgeable 157 7.5 2100 Moderately knowledgeable 763 36.3 Knowledgeable 1009 48.0 Very knowledgeable 171 8.1 79 Attitudes The first attitude question (Question 14) was directed toward only those students who indicated that they had at least one credit card in their name. Students were asked to respond to 11 statements, listed in Table 4.4, regarding how they viewed and used credit cards (N = 1485). An index of attitudes was computed for the items measuring college students' attitudes toward use and view of credit cards, and mean values were calculated for the index scores. The results are shown in Table 4.5. Overall, the responses indicated that college students had a healthy attitude toward the use and view of credit cards, with a combined item mean of 2.30 on a scale ranging from 4 = Strongly Agree (unhealthy attitude) to 1 = Strongly Disagree (healthy attitude). The reliability coefficient of the index was .75. When the responses for Strongly Agree and Agree were summed, the statements rec^eiving the highest amount of support were, "Whenever I use a credit card I think about what I owe" (79.8%) and "Credit cards can get me into financial trouble" (72.9%). Only12.5% of the students supported the statement, "I view my credit card purchases as spending the income I will make when I graduate," and only 10.4% of the students supported the statement, "I don't need to think about paying off credit card bill(s) until I am out of college." 80 Table 4.4. Attitudes of College Students Who Use Credit Cards. Statement Strongly Agree Disagree Strongly N Agree Disagree n % n % n % n % Whenever 1 use a credit 528 35.6 656 44.2 181 12.2 118 8.0 1483 card 1 think about what 1 owe credit cards can get me 617 41.5 466 31.4 232 15.6 170 11.4 1485 into financial trouble 1 don't need to think about 44 3.0 110 7.4 408 27.5 922 62.1 1484 paying off credit cards until I'm out of college With credit cards 1 don't 102 6.9 498 33.6 535 36.1 346 23.4 1481 have to wait to buy the things 1 want Having credit card bills 391 26.4 610 41.2 302 20.4 177 12.0 1480 can be discouraging/ depressing V\/hen stressed out 1 have 112 7.5 277 18.7 404 27.2 691 46.6 1484 a greater tendency to go out and buy things using credit cards Credit cards make it easy 265 17.9 661 44.8 276 18.7 275 18.6 1477 to buy things 1 do not need 1 view my credit card 42 2.8 144 9.7 515 34.8 780 52.7 1481 purchases as spending the income I'll make when 1 graduate Whenever 1 use a credit 281 19.1 501 34.0 418 28.4 273 18.5 1473 card 1 worry about paying it off It's ok to make just the 37 2.5 209 14.1 584 39.4 653 44.0 1483 minimum payment on my credit card every month Using credit cards tempts 180 12.1 526 35.4 414 27.9 364 24.5 1484 me to purchase more items 81 The second attitude question (Question 16) was directed toward all students, whether or not they had a credit card. An index of attitudes was computed for the nine items measuring college students' attitudes toward acquisition and use of credit cards. Items worded negatively were reverse coded to enhance the strength of the index. Mean values were calculated for the index scores, and the results are shown in Table 4.6. Overall, the responses indicated that college students had a negative attitude toward acquisition and use of credit cards, with a combined item mean of 2.26 on a scale ranging from 4 = Strongly Agree (positive attitude) to 1 = Strongly Disagree (negative attitude). The reliability coefficient of the index was .65. Of the 2,105 students responding to the question measuring college students' attitudes toward the acquisition and use of credit cards, more than 90% of college students agreed or strongly agreed with the statement, "Using a credit card will be helpful for building a credit history" (Table 4.7). Sixty-five percent of the students agreed or strongly agreed that, "It is unwise to use credit cards." More than 80% of the students disagreed or strongly disagreed with the statements, "I am happy to see credit card issuers on campus" (86.4%), "I want to acquire more credit cards than I have now" (84%), and "Heavy use of credit cards results in heavy debt" (82.6%). 83 Table 4.6. Mean Values for Responses to Acquisition and Use of Credit Cards Scale. Statement Mean N SD It is unwise to use credit cards. 2.65 2089 0.82 The cost of using credit cards is too high. 2.36 2099 0.79 1 dislike using credit cards. 2.52 2094 0.88 Heavy use of credit cards results in heavy debt. 1.72 2097 0.83 1 am happy to see credit card issuers on campus. 1.67 2090 0.74 1 want to acquire more credit cards than 1 have now. 1.68 2103 0.80 Credit card companies should not be allowed to market 2.43 2102 0.84 their products on campus. Using a credit card will be helpful for building a credit 3.24 2100 0.66 history. 1 am not tempted by discounts or other incentives to 2.13 2105 0.93 acquire a credit card Index Mean: 2.26 Note: 4 = Strongly Agree, 1 = Strongly Disagree 84 Table 4.7. Students' Attitudes toward Acquisition and Use of Credit Cards. statement Strongly Agree Disagree Strongly N Agree Disagree n % n % n % n % 1 am not tempted by 177 8.4 530 25.2 788 37.4 610 29.0 2105 incentives to acquire card 1 want to acquire more 53 2.5 285 13.6 691 32.9 1074 51.1 2103 credit cards 1 am happy to see credit 28 1.3 256 12.2 798 38.2 1008 48.2 2090 card issuers on campus 1 dislike using credit cards 239 11.4 922 44.0 624 29.8 309 14.8 2094 It is unwise to use credit 233 11.2 1115 53.4 513 24.6 228 10.9 2089 cards Heavy use of credit cards 72 3.4 292 13.9 715 34.1 1018 48.5 2097 results in heavy debt Using a credit card will 726 34.6 1190 56.7 140 6.7 44 2.1 2100 help build a credit history The cost of using credit 90 4.3 898 42.8 796 37.9 315 15.0 2099 cards is too high Credit card companies 135 6.4 988 47.0 627 29.8 352 16.7 2102 should not be allowed to market on campus Descriptive Statistics of Output Variables Credit card practices (the output variable) includes college students' reasons for owning and using cards, number of cards held, types of purchases, and students' repayment practices. Some of these areas will be discussed more fully in the section on results related to the research questions. A summary of the descriptive statistics for college students' credit card practices is shown in Table 4.8. 85 Table 4.8. Descriptive Statistics of the Output Variables. Variables Categories n % N Students with at least one card Yes 1486 70.3 2113 No 627 29.7 Period first card obtained Before entering college 706 47.6 1483 During first year of college 496 33.4 After first year of college 281 18.9 Age first card obtained Less than 18 300 20.2 1483 18-19 931 62.8 20-21 203 13.7 Over 21 49 3.3 Number of credit cards 1 829 59.2 1400 2 361 25.8 3 148 10.6 More than 3 62 4.4 Number of store cards 1 279 50.5 552 2 149 27.0 More than 3 124 22.5 Number of gas card 1 242 65.8 368 2 93 25.3 More than 2 33 9.0 Combined number of cards 1 534 36.2 1475 2 340 23.1 3 250 16.9 4 140 9.5 More than 4 211 14.3 Monthly credit card use 0-3 times 601 43.2 1391 4-6 times 304 21.9 7-9 times 134 9.6 More than 10 times 352 25.3 Monthly store card use 0-3 times 499 92.1 542 4-6 times 34 6.3 7-9 times 6 1.1 More than 10 times 3 0.6 Monthly gas card use 0-3 times 162 44.0 368 4-6 times 147 39.9 7-9 times 36 9.8 More than 10 times 23 6.3 86 Table 4.8. Continued. Variables Categories n % N Monthly charges to credit $0 1347 card 124 9.2 $1-$100 660 49.0 $101-$200 227 16.9 $201-$300 120 8.9 $301-$400 49 3.6 $401-$500 71 5.3 More than $500 96 7.1 Monthly charges to store card $0 113 21.8 518 $1-$50 263 50.8 $51-$100 98 18.9 More than $100 44 8.5 Monthly charges to gas card $0 18 5.2 349 $1-$50 102 29.2 $51-$100 153 43.8 More than $100 76 21.8 Highest APR for credit card 1.0%-10% 205 26.5 773 10.1%-20% 461 59.6 20.1%-30% 107 13.8 Highest APR for store card 1.0%-10% 23 10.4 222 10.1%-20% 106 47.7 20.1%-30% 93 41.9 Highest APR for gas card 1.0%-10% 25 32.1 78 10.1%-20% 40 51.3 20.1%-30% 13 16.7 Total credit limit Under $1,000 328 25.0 1312 $1,000-$2,999 370 28.2 $3,000-$4,999 183 13.9 $5,000-$6,999 144 11.0 $7,000-$8,999 53 4.0 $9,000-$10,999 82 6.3 More than$10,999 152 11.6 87 Table 4.8. Continued. Variables Categories n % N Pay major part of bill Self 888 60.7 1463 Spouse 33 2.0 Parents 542 37.0 Monthly payment practices Minimum 159 10.8 1467 More than minimum 457 31.2 In full 681 46.4 Some in full 129 8.8 Juggle payments 18 1.2 Unable to pay minimum 23 1.6 Heard about Red to Black Yes 633 30.5 2073 No 1440 69.5 Used services of Red to Black Yes 18 0.9 2072 No 2054 99.1 Of the total sample (2,113), 1,486 or 70.3% reported having at least one credit card in their name. Of these, 706 (47.6%) indicated that they had received their first card before they entered college, while one third of the students (496) had received their first card during the first year of college. More than 80% (1,231) of the students reported that they were under the age of 20 when they received their first card, and 20.2% of these were less than 18 years old. Only 3.3% reported that they had been over 21 when they acquired their first card. Students were asked to report on the number of credit cards, store cards, and gas cards they held. When the students' total number of cards was computed, results indicate that 36.2% held at least one card, while 14.3% held five or more cards. Among the 14.3% with five or more cards, 12 students 88 reported having more than 10 cards. The annual percentage rate on the cards ranged from 0% for an introductory rate to 29.99%, with the majority of the students reporting rates between 10.1% and 20%. Students also reported on the number of times each month they used their credit, store, and gas cards. As shown in Table 4.8, when card usage in the 4 to 6 times a month category is compared, the results show that more students reported using credit cards (304 students) and gas cards (147 students) than store cards (34 students). Three hundred and fifty-two students, or 25.3% of credit card users, indicated that they used their credit cards more than 10 times each month, compared with 23 students who used gas cards, and 3 students who used store cards. Students' card usage is discussed further under Research Question 3. The majority of students (approximately 70% or 361) making purchases using gas and store cards reported that they charged $100 or less to their cards each month, while almost half (49% or 660) of the students reported that they charged under $100 a month on their credit cards. Seven percent charged more than $500 to their credit cards each month. Charges made to cards ranged from $5 to as high as $8000 a month for credit cards, $1,500 a month for store cards, and $500 a month for gas cards. The majority of students (78.1%) reported having a total credit limit of $7,000 or less. More than 11% indicated that their credit limit was $11,000 or more, with 57 students (4.3%) reporting a limit of more than $25,000. 89 In reporting their repayment practices, the majority of students indicated that they were responsible for paying the major part of their credit card bills, while one third of the students reported that parents paid the major part of their bills. Forty-six percent of respondents indicated that they paid their credit card bills in full each month, while 10.8% paid the minimum payment and 1.6% reported they were unable to pay the minimum required. Students were asked to respond to questions regarding the newly implemented Red to Black financial educational and counseling program at TTU. Six hundred and thirty-three students (30.5%) answered that they had heard about it, while less than 1% had used its services. Students who answered negative to Question 1 (Do you have at least one credit card in your own name?) were asked to respond to the question, "Why do you not have a credit card in your own name?" Of the 627 students who responded that they had no credit cards in their name, 17.4% reported that they use someone else's card and did not need their own (see Table 4.9). Forty-one percent of the students indicated that they would apply for a card later, while 34.3% responded that they had not applied for a card and did not intend to do so. Approximately 13% of the students reported that they used to own a card but had destroyed or cancelled it. 90 Table 4.9. College Students' Reasons for Not Having Credit Cards (N = 627). Statement n % 1 have not applied for one, and 1 do not intend to. 215 34.3 1 have not applied for one but intend to do so at a later date. 259 41.3 I've just applied for one but have not received it. 12 1.9 My application has been rejected. 35 5.6 1 used to have at least one credit card, but 1 have destroyed/ 81 12.9 cancelled it. 1 use someone else's card, and 1 do not need my own. 109 17.4 Other 103 16.4 Results Related to the Research Questions Question 1 How did college students acquire their credit cards? Table 4.10 provides descriptive analyses of the factors that most influenced students to apply for their first card. This question was aimed at eliciting information about the marketing strategies which were most effective in influencing students. Using choices ranging from Extremely Influential (4) to Not at all Influential (1), respondents were asked to indicate the amount of influence each factor had on their obtaining their first credit card. When the Extremely Influential and Clearly Influential categories were summed, the most influential 91 factors were the suggestion of parents (57.2%), receiving information in the mail (31%), and wanting the gifts/incentives that came with the card (17.5%). Table 4.10. Reasons College Students Acquire Credit Cards. Variable Extremely Clearly Somewhat Not at all Influential Influential Influential Influential n % n % n % n % N Telemarketer 27 1.8 22 1.5 53 3.6 1372 93.1 1474 Mail 192 13.0 266 18.0 231 5.7 786 53.3 1475 Campus bulletin board 34 2.3 44 3.0 101 6.9 1285 87.8 1464 Parents 552 37.6 288 19.6 130 8.8 499 34.0 1469 Campus promotion 78 5.3 64 4.3 95 6.4 1238 83.9 1475 Intemet advertisement 16 1.1 37 2.5 81 5.6 1335 90.8 1470 Friends 54 3.7 155 10.5 266 18.1 996 67.7 1471 Radio/TV ad 18 1.2 36 2.4 81 5.5 1338 90.8 1473 Newspaper/Magazine 8 0.5 30 2.0 80 5.4 1350 92.0 1468 Gifts/Incentives 102 6.9 156 10.6 153 10.4 1062 72.1 1473 Other 266 31.6 66 7.8 14 1.7 497 59.0 843 Over 90% of respondents indicated that the following factors were not at all influential in their decision to acquire a credit card: Contacted by a telemarketer (93.1%), advertisement in a newspaper/magazine (92%), advertisement on the Internet (90.8%), and advertisement on the radio/television (90.8%). Only 843 students (N = 1486) responded to the "Other" item. Of these, 39.4% reported that it was extremely influential. Among the comments recorded 92 by students was that they had received their card as part of a package of services from a bank when they opened an account. In addition, several students indicated that they had applied for the card in case of an emergency. This may be interpreted to mean that it was the students' own idea because of a perceived need. Question 2 What reasons do college students give for using credit cards? Frequencies and percentages were used to analyze this question. Using choices ranging from Very Important (4) to Not at all Important (1), respondents were asked to indicate the importance of each of 11 factors as a reason for using their credit cards. These findings are summarized in Table 4.11. When students' responses to Important and Very Important were summed, to establish a credit record (71.6%), to cover emergency needs (66.8%), and for convenience (6i3.6%) were the most important reasons cited for using credit cards. To keep up with my friends (87.9%) and to get cash advances (82.1%) were cited most by students as being Not at all Important. 93 Table 4.11. Reasons College Students Use Credit Cards. Variable Very Important Somewhat Not at all Important Important Important n % n % n % n % N Cash advances 38 2.6 61 4.1 166 11.2 1218 82.1 1483 Buy now, pay later 208 14.0 373 25.2 415 28.0 486 32.8 1482 Convenience 449 30.4 490 33.2 319 21.6 220 14.9 1478 Safety 318 21.5 376 25.4 354 23.9 434 29.3 1482 Emergency needs 540 36.4 450 30.4 260 17.5 232 15.7 1482 Ease in making returns 44 3.0 74 5.0 280 18.9 1083 73.1 1481 Establish credit record 674 45.5 387 26.1 241 16.3 180 12.1 1482 Incentives 110 7.4 145 9.8 296 20.0 928 62.7 1479 Borrow for college 65 4.4 106 7.2 175 11.8 1135 76.6 1481 Make ends meet 96 6.5 176 11.9 291 19.6 921 62.1 1484 Keep up with friends 20 1.3 44 3.0 115 7.8 1304 87.9 1483 Question 3 What types of purchases do college students make most with credit cards? Table 4.12 provides descriptive analyses of the types of purchases college students make most with credit cards. Using choices ranging from Often (4) to Never (1), respondents were asked to indicate how often they used credit cards 94 Table 4.12. College Students' Level of Credit Card Use. Variable Often Sometimes Hardly Ever Never n % n % n % n % N Utility/Phone bills 45 3.0 60 4.1 91 6.2 1282 86.7 1478 Groceries 383 25.9 446 30.2 266 18.0 384 26.0 1479 Tuition/Fees 217 14.7 211 14.3 140 9.5 911 61.6 1479 Insurance 13 0.9 21 1.4 64 4.3 1381 65.4 1479 Rent/Mortgage 15 1.0 18 1.2 43 2.9 1399 94.8 1475 Textbook/Supplies 500 33.8 409 27.6 186 12.6 385 26.0 1480 Entertainment/Sports 303 20.5 374 25.3 249 16.8 552 37.3 1478 Eating out 439 29.6 430 29.0 257 17.4 355 24.0 1481 Cash advances 68 4.6 75 5.1 159 10.8 1170 79.5 1472 Emergencies 249 16.9 324 21.9 347 23.5 557 37.7 1477 Vacation/Travel 278 18.8 349 23.7 231 15.7 617 41.8 1475 Clothing 367 24.8 468 31.6 281 19.0 366 24.7 1482 to pay for several different kinds of merchandise and services as well as to obtain cash advances. A third of the students with credit cards indicated that they often used their credit cards. When the Often and Sometimes categories were added together, the most frequently cited item purchased with credit cards was 95 textbooks/school supplies (61.4%). Eating out (58.6%), clothing (56.4%), and groceries (56.1%) were the next highest items. Insurance (2.3%) and rent/mortgage (2.2%) were the items paid for least frequently with credit cards. Ninety-five percent of the students indicated that they never paid their rent/mortgage with credit cards, while 86.7% never paid their utility/telephone bills with credit cards. Table 4.13 provides descriptive analyses of college students' cash advance purchases. Of the 238 students who reported that they had obtained cash advances during the past year, 46.6% cited entertainment and 28.2% cited groceries/ household items as the items for which they had used the cash advances the most. Insurance (0.4%), housing (7.1%), and payment for another credit card (7.1%) were the items least likely to have been paid for with cash advances. Table 4.13. College Students' Cash Advance Purchases. Variables n % N Payment for another credit card 17 7.1 238 Utility/Phone bills 20 8.4 238 Education-related expenses 32 13.4 238 Groceries/Household items 67 28.2 238 Rent/Mortgage 17 7.1 238 Insurance 1 0.4 238 Emergency 33 13.9 238 Entertainment 111 46.6 238 Other 48 20.3 237 96 Question 4 Where do college students obtain their knowledge of personal finance/ money management principles? Table 4.14 provides a summary of the findings of college students' sources of personal finance/money management principles. Of the 1,885 students who responded to the question, "What has been your primary source of personal financial/money management information?" the majority (83.5%) answered that they had received their financial knowledge from parents. The next highest source of knowledge was a course in college (4.9%). Among the 4.6% of respondents who indicated that their knowledge came from another source (Other), several cited personal experience as the source. Of the 2,113 students who completed the survey, 726 (34.5%) responded that they had taken a course covering topics in personal finance/money management principles. College was cited most frequently by students (71.3%) as the institution where the course had been taken, while economics (61.8%) and business (52.4%) were named most frequently as the subject area where students had taken the course. Only 23.1% of the respondents named family and consumer sciences as the course where they had taken personal finance/money management principles. This number may be higher, however, as many of the students (19.2%) said they had taken it in another subject and cited personal financial planning as the subject in college where they had taken a course in personal finance/money management. Personal financial planning is a course offered by 97 the College of Human Sciences at TTU, and it comes under the umbrella of family and consumer sciences. Table 4.14. College Students' Sources of Financial Knowledge. Variables n % N Course School 255 35.3 722 College 515 71.3 722 Other 23 3.2 722 Type of Course Business 379 52.4 723 Economics 447 61.8 723 Family & Consumer Sciences 167 23.1 723 Social Studies 105 14.5 723 Other 139 19.2 723 Primary Source of Information Financial services 20 1.1 1885 Literature 32 1.7 1885 Friends 43 2.3 1885 Parents 1574 83.5 1885 Course in high school 26 1.4 1885 Course in college 92 4.9 1885 Internet 11 0.6 1885 Other 87 4.6 1885 Question 5 What are college students' attitudes toward financial education and financial counseling? Four items in Question16 on the questionnaire addressed students' attitudes toward financial education and counseling. For the purpose of the data 98 analysis, these items were treated separately. One item was negatively worded and was receded for the purpose of the analysis. The data are summarized in Table 4.15. Table 4.15. College Students' Attitudes toward Financial Counseling and Education. Variables Strongly Agree Disagree Strongly Agree Disagree n % n % n % n % N Colleges should have 841 40.0 1069 50.9 155 7.4 37 1.8 2102 credit counseling available 1 w/ould not consult a credit 270 12.9 737 35.1 862 41.0 231 11.0 2100 counselor on campus Taking a course in 416 19.8 1046 49.8 488 23.2 151 7.2 2101 personal financial management would help me All freshmen should be 434 20.6 713 33.9 666 31.7 290 13.8 2103 required to take a personal financial management course Students generally had favorable attitudes toward financial counseling and financial education; however, fewer students agreed or strongly agreed when asked if they would act on their beliefs. Whereas a large majority of students (90.9%) agreed that colleges should have credit counseling available for all students, less than half of the respondents (48%) indicated that they would personally consult a credit counselor on campus. Seventy percent (N = 1462) of 99 the students believed that taking a course in personal financial management would help them better manage their finances; however, only 54.5% agreed that all freshmen should be required to take a course in personal financial management. Question 6 What is the debt level of college students as it relates to credit card debt, student loan debt, and other consumer debt? Frequencies and percentages were used to analyze the data on students' debt levels, and these are summarized in Table 4.16. Of the 1,336 respondents who had a bank, store, or gas card, 35.9% reported that they had no debt, while 40.3% owed less than $1,001. Fourteen percent of the students owed more than $2,001, with the highest reported total card balance being $44,000. The mean was $1,036.08. There were 341 non-responses from among respondents who held at least one card, indicating either a lack of knowledge of their credit card balances, or an unwillingness to reveal the amount. Almost 50% of the students surveyed indicated that they had no consumer debt (48.8%). Of the remaining students with consumer debt balances, 15.1% had balances totaling less than $1,000, while 13.4% had balances of over $7000. More than half the respondents reported that they had no student loans (52.8%); however, almost 25% had a student loan debt totaling more than $7,000. Approximately 8% of the students answered, "I'm not sure," when asked to report on their student loan debt and their consumer loan debt. 100 Table 4.16. College Students' Debt Level. Variables Categories n % N Credit card balance $0 474 37.7 1258 $1-$1,000 511 40.6 $1,001-$,2000 114 9.1 $2,001 - $3,000 64 5.1 More than $3,000 95 7.6 Store card balance $0 239 49.1 487 $1 -$500 186 38.2 More than $500 62 12.7 ; Gas card balance No balance 197 73.0 270 Balance 73 27.0 Total card debt $0 479 35.9 1336 $1 -$1,000 539 40.3 $1,001 -$2,000 130 9.7 $2,001 - $3,000 76 5.7 More than $3,000 112 8.4 Consumer debt $0 1011 48.8 2072 Less than $1,000 312 15.1 $1,000-$2,999 166 8.0 $3,000-$4,999 91 4.4 $5,000-$6,999 59 2.8 $7,000 and over 276 13.4 Not sure 157 7.6 Student loan debt $0 1106 52.8 2093 Less than $1,000 29 1.4 $1,000-$2,999 97 4.6 $3,000-$4,999 106 5.1 $5,000-$6,999 119 5.7 $7,000 and over 480 23.0 Not sure 156 7.5 101 Question 7 What is the relationship between selected socio-demographic variables (age, gender, ethnicity, marital status, classification, major, grade point average, employment, income, sources of income, and socioeconomic background) and (a) college students' credit card knowledge and (b) college students' credit card attitudes? Stepwise multiple regression analyses were used to describe college students' socio-demographic characteristics as they relate to credit card knowledge and attitudes. As shown in Table 3.1, the independent variables gender, ethnicity, marital status, classification, and major were dummy coded for the analysis. An index of unhealthy/healthy attitudes toward use and view of credit cards was computed for questionnaire item #14, which was answered by only those students who held at least one credit card in their name. An index of college students' positive/negative attitudes toward acquiring and using credit cards was computed for questionnaire item #16, which was answered by all students. Cases with missing variables were excluded on a pairwise basis, which means that when subjects had missing variables, their data were excluded only for the calculations involving the missing variable. College Students' Knowledge Table 4.17 shows the regression results for credit card knowledge. The R square revealed that 11 socio-demographic characteristics explained 11.5% of the variance of the dependent variable, knowledge. Thus, the R square suggests there were additional factors contributing to the variance of college students' 102 Table 4.17. Regression Results with Socio-Demographic Characteristics as the Independent Variables and College Students' Knowledge of Credit Cards as the Dependent Variable (N = 1621). Variable b SEB Beta t Sig Constant 9.519 .451 21.087 .000 Parents' income .125 .023 .132*** 5.439 .000 Financial support from job .676 .136 .120*** 4.972 .000 Financial support from scholarships .574 .130 .110*** 4.417 .000 Business majors .469 .134 .085*** 3.509 .000 Freshman classification -1.129 .248 -.110*** ^.557 .000 Sophomore classification -.712 .172 -.100*** -4.134 .000 Ethnic background (white) .568 .172 .079** 3.308 .001 Grade Point Average .420 .134 .076** 3.131 .002 Financial support from interest from .557 .227 .059* 2.460 .014 savings/investments Entering freshman classification -.474 .227 -.051* -2.093 .037 Engineering/Architecture majors .431 .211 .049* 2.046 .041 R2=.115 F-19.024* * p < .05 ** p < .01 *** p < .001 knowledge other than the 11 independent variables shown. Parents' income, financial support from job and scholarships, and being a business major were all significant at the .001 level. Ethnicity and grade point average were significant at the .01 level. Students' classification, freshman and sophomore, had a negative relationship with credit card knowledge at the .001 level, while entering freshman 103 had a negative relationship at the .05 level. Students who were entering freshmen, freshmen, and sophomores appeared to have low levels of credit card knowledge than did juniors and seniors. Attitudes toward Use of Credit Cards When college students' attitudes (unhealthy/healthy) toward use and view of credit cards were analyzed using a regression analysis, 12.2% of the variance of the dependent variable was explained (Table 4.18). The R square suggests that other factors existed which contributed to the variance of the dependent variable, college students' attitudes toward use of credit cards. Of the eight socio-demographic characteristics shown to be significant, the most significant at the .001 level was financial support from credit cards/cash advances. Also significant at the .001 level was the variable parents' credit-related problems. Students whose financial support came from credit cards/cash advances and who had observed their parents having credit-related problems were likely to have unhealthy attitudes toward the use and view of credit cards. Grade point average, parents' income, and father's level of education had a negative relationship with students' attitudes toward use of credit cards. Students with a high grade point average, whose parents were from a high income bracket, and whose fathers were highly educated were more likely than other students to have a healthy attitude toward the use and view of credit cards. 104 Table 4.18. Regression Results with Socio-Demographic Characteristics as the Independent Variables and College Students' Attitudes toward Use of Credit Cards as the Dependent Variable (N = 1278). Variable b SE Beta t Sig. Constant 31.219 1.146 27.253 .000 Financial support from credit cards/ 3.017 .426 .190*** 7.085 .000 cash advances Grade Point Average -1.829 .316 -.154*** -5.796 .000 Parents' credit-related problems .673 .167 110*** 4.040 .000 Senior classification 1.054 .304 .094** 3.466 .001 Parents' income -.235 .058 -.115*** -4.053 .000 Father's level of education -.303 .105 -.077** -2.881 .004 Financial support from spouse/ 1.625 .599 .076** 2.712 .007 partner Financial support from interest from -1.174 .542 -.058* -2.164 .031 savings/investments R=^ = .122 F = 22.146*** B* = <.05 Q** = <.01 2*** = <.001 Attitudes toward Acguisition and Use of Credit Cards As indicated in Table 4.19, nine socio-demographic variables were significant in relation to college students' attitudes (positive/negative) toward acquisition and use of credit cards. However, the R square suggests that only 7.9% of the dependent variable was explained by the independent variables. Parents' use of credit cards was the most significant of these variables at the 105 Table 4.19. Regression Results with Socio-Demographic Characteristics as the Independent Variables and College Students' Attitudes toward Acquisition and Use of Credit Cards as the Dependent Variable (N = 1621). Variable b SE Beta t Sig. Constant 18.094 .509 35.524 .000 Parents' use of credit cards .723 .105 .168*** 6.888 .000 Parents' credit-related problems -.349 .111 -.081** -3.153 .002 Never married 1.071 .317 .083** 3.381 .001 Business majors .673 .199 .081** 3.382 .001 Financial support from credit cards/ .930 .276 .083** 3.365 .001 cash advances Senior classification -.434 .193 -.055* -2.246 .025 Financial support from other sources 1.362 .633 .052* 2.151 .032 Number of hours employed -.131 .065 -.050* -2.007 .045 Financial support from financial aid -.412 .209 -.052* -1.974 .049 R2 = .079 F= 15.396*** Q* = <.05 B** = <.01 g*** = <.001 .001 level. In addition, marital status, major, and source of income were significant at the .01 level. Thus, students who were business majors, had never been married, and whose sources of income included credit cards/cash advances were likely to have a positive attitude toward acquisition and use of 106 credit cards. Students with parents who had experienced credit related problems were likely to have negative attitudes toward credit cards acquisition and use. Question 8 What is the relationship between selected socio-demographic variables (age, gender, ethnicity, marital status, classification, major, grade point average, employment, income, sources of income, and socioeconomic background) and college students' credit card practices (reasons for using credit cards, number of cards, types of purchases, repayment practices)? Stepwise multiple regression analyses were used to explain college students' socio-demographic characteristics as they relate to credit card practices. Cases with missing data were excluded on a pain^/ise basis, so that when subjects had missing variables, their data were excluded only for the calculations involving the missing variable. As shown in Table 3.1, the independent variables gender, ethnicity, marital status, classification, and academic major were dummy coded for the analyses. To aid in the multiple regression analyses, factor analyses were used to reduce the number of variables measuring why college students use credit cards and the types of items purchased (see Tables 3.2 and 3.3). Table 3.2 shows the results of the factor analysis for Questionnaire Item #13, reasons for college students' use of credit cards. Three factors, Convenience/Incentives, Support for Lifestyle/Education, and Emergency/Credit Record, were identified, each with loadings of .50 or higher. Table 3.3 shows the results of the factor analysis for Questionnaire Item #7, types of credit card purchases made by 107 college students. Three factors, Food/Clothing/Leisure, Education/Emergency, and Housing/Cash Advances, were identified. The loadings of each factor were .50 or greater. Reasons for Using Credit Cards-Convenience/Incentives The results of the multiple regression analysis, presented in Table 4.20 revealed that six socio-demographic variables were retained in the regression model, indicating they have a significant relationship with using credit cards for the purpose of convenience/receiving incentives. Eight percent of the variance of the dependent variable was explained by parents' use of credit cards, parents' household income, students' income, students' financial support from savings, and students' majors (Business and Arts & Sciences/Visual & Performing Arts/ Education). The R square suggests there were additional factors that contributed to the variance of students' use of credit cards for convenience/incentives. Students' responses indicated that parents' use of credit cards and parents' income were the most significant variables at the .001 level. The least significant at the .05 level were Arts & Sciences/Visual & Performing Arts/Education majors, and students' income. This suggests that students who had parents with high incomes and those whose parents used credit often when the students were growing up in the home were more likely to use credit cards for convenience/ incentives compared to other students. 108 Table 4.20. Regression Results with Socio-Demographic Characteristics as the Independent Variables and College Students' Reasons for Using Credit Cards (Convenience/Incentives) as the Dependent Variable (N = 1277). Variable SE t Sig. lo r Beta Constant 4.936 .325 15.182 .000 Parents' use of credit cards .539 .083 .180*** 6.458 .000 Parents' income .271 .061 .125*** 4.466 .000 Financial support from savings .439 .149 .080** 2.957 .003 Business majors .510 .168 .089** 3.042 .002 Arts & Sciences/Visual & Performing .407 .182 .065* 2.241 .025 Arts/Education majors Students' income .056 .027 .056* 2.070 .039 R2 = .081 F= 18.603*** Q* = <.05 e** = <.01 Q*** = <.001 Reasons for Using Credit Cards-Support for Lifestyle/ College Education With support for lifestyle/college education (college students' reasons for using credit cards) as the dependent variable, a regression analysis identified eight socio-demographic variables as significant. The results are reported in Table 4.21. The eight socio-demographic variables explained 16.3% of the variance of the dependent variable. Thus the R square suggests that additional 109 factors explained the variance of college students' use of credit cards to support lifestyle/college education. Table 4.21. Regression Results with Socio-Demographic Characteristics as the Independent Variables and College Students' Reasons for Using Credit Cards (Support for Lifestyle/College Education) as the Dependent Variable (N = 1276). Variable b SE Beta t Sig. Constant 9.771 .507 19.283 .000 Financial support from credit cards/ 1.905 .191 .262*** 9.950 .000 cash advances Grade Point Average -.814 .142 -.150*** -5.739 .000 Ethnic background (white) -.840 .186 -.118*** -4.520 .000 Parents' credit-related problems .312 .074 .111*** 4.222 .000 Arts & Sciences/Visual & Performing .637 .163 .108*** 3.896 .000 Arts/Education majors Number of hours employed .111 .045 .065* 2.484 .013 Business majors .390 .149 .072* 2.611 .009 Financial support from savings -.315 .135 -.061* -2.337 .020 R^=.163 F = 30.841*** B* = <.05 e** = <.01 2*** = <.001 Students' grade point average and ethnic background were two variables that negatively influenced students' use of credit cards to support their lifestyle/ education purposes. Thus, the higher the grade point average, the less likely 110 students were to use credit cards to support their lifestyle and education needs. In contrast, parents' credit-related problems and students' financial support from credit cards/cash advances were the most likely to predict students' use of credit cards for the stated reason. Business majors and students' financial support from savings represented the lowest level of significance at the .05 level. Reasons for Using Credit Card-Emergency/Credit Record Five independent variables were retained by the stepwise regression analysis as being significant in explaining emergency/credit record as a factor in college students' reasons for using credit cards. Table 4.22 shows that the higher the levels of financial support from savings and financial aid and the greater the parents' use of credit cards, the more likely students were to use credit cards mainly for the purpose of emergency/credit record. These variables were the most significant at the .01 level. The variables parents' income and junior classification status also were significant at the .05 level. The five independent variables explained only 2.7% of the variance of the dependent variable, thus suggesting the existence of other factors that explained the variance of emergency/credit record as a reason for college students' use of credit cards. Ill Table 4.22. Regression Results with Socio-Demographic Characteristics as the Independent Variables and College Students' Reasons for Using Credit Cards (Emergency/Credit Record) as the Dependent Variable (N = 1275). Variable b SE Beta t Sig. Constant 4.871 .218 22.337 .000 Financial support from savings .292 .095 .085** 3.078 .002 Parents' use of credit cards .148 .053 .079** 2.765 .006 Junior classification .248 .097 .070* 2.545 .011 Financial support from financial aid .273 .103 .079** 2.661 .008 Parents' income .088 .041 .065* 2.134 .033 R=^ = .027 F = 7.086*** E* = <.05 B** = <.01 Q*** = <.001 Number of Credit Cards Table 4.23 reveals that five independent variables explained 9.5% of the variance. The R square indicates there were additional factors contributing to the variance of number of credit cards held by students. Students' income levels and gender were the most significant variables (£ < .001) in explaining the number of credit cards students possessed. Financial support from credit cards/cash advances and senior classification were significant at the .01 level. Parents' credit-related problems and the number of hours students were employed also were significant at the .05 level of significance. Ethnic background was 112 negatively correlated with number of credit cards; in other words, whites were less likely to have a large number of credit cards when compared to other ethnic groups. Table 4.23. Regression Results with Socio-Demographic Characteristics as the Independent Variables and Number of Credit Cards as the Dependent Variable (N = 1270). Variable b SE Beta t Sig. Constant 1.535 .208 7.369 .000 Students' income .109 .021 .150*** 5.280 .000 Gender (female) .583 .105 .150*** 5.571 .000 Financial support from credit cards/ .531 .156 .094** 3.396 .001 cash advances Senior classification .346 .111 .086** 3.122 .002 Parents' credit-related problems .152 .060 .069* 2.539 .011 Number of hours employed .083 .038 .063* 2.210 .027 Ethnic background (white) -.295 .150 -.053* -1.966 .049 R2 = .095 F= 18.835*** 2* = <.05 2** = <.01 Q*** = <.001 113 Credit Card Purchases-Food/Clothing/Leisure The regression results of college students' credit card food/clothing/leisure purchases (Table 4.24) showed that 10.6% of the variance was explained by the seven independent variables retained by the regression model. Thus, R square suggests there were additional factors which explained the variance of food/ clothing/leisure expenses as a type of college students' credit card purchases. Table 4.24. Regression Results with Socio-Demographic Characteristics as the Independent Variables and College Students' Credit Card Purchases (Food/ Clothing/Leisure) as the Dependent Variable (N = 1277). Variable b SE Beta t Sig. Constant 8.328 .624 13.353 .000 Parents' use of credit cards .850 .134 175*** 6.320 .000 Financial support from credit cards/ 2.123 .343 .169*** 6.198 .000 cash advances Parents' income .349 .108 .099** 3.233 .001 Financial support from financial aid -.639 .268 -.071* -2.389 .017 Business majors .583 .249 .063* 2.340 .019 Students' income .100 .043 .062* 2.304 .021 Parents' credit-related problems -.322 .145 -.066* -2.223 .026 R^=.106 F = 21.576*** e* = <.05 Q** = <.01 Q*** - <.001 114 Parents' use of credit cards and financial support from credit cards/cash advances were the most significant variables in explaining students' credit card food/clothing/leisure purchases. These were significant at the .001 level. The next significant variable in explaining students' use of credit cards for food/clothing/leisure purchases was parents' income level. As shown in Table 4.24, the variable, financial support from financial aid, had a negative relationship with students' use of credit cards for food/clothing/leisure purchases at the .01 significance level. Also, parents' credit-related problems indicated a negative relationship with college students' credit card food/clothing/leisure purchases, thereby suggesting that students whose parents had the highest credit-card related problems would be the least likely to use credit cards for food/clothing/leisure purchases. Credit Card Purchases-Education/Emergency As indicated in Table 4.25, parents' use of credit cards and students' financial support from credit cards/cash advances were the most significant variables in explaining students' use of credit cards for education/emergency purposes. These variables were significant at the .001 level. Similarly, freshman and entering freshman classifications, as well as students' financial support from job were significant at the .001 levels. However, these all had a negative relationship with the dependent variable, college students' credit card education/emergency purchases. Likewise, there were negative relationships 115 with financial support from financial aid and Human Sciences majors, both of which were reported at the .05 significance level. Only 7.4% of the variance was explained by the eight independent variables. Therefore, R square indicates there were additional factors that explained the variance in college students' credit card education/emergency purchases. Table 4.25. Regression Results with Socio-Demographic Characteristics as the Independent Variables and College Students' Credit Card Purchases (Education/ Emergency) as the Dependent Variable (N =1275). Variable b SE Beta t Sig. Constant 6.290 .285 22.040 .000 Parents' use of credit cards .363 .082 .122*** 4.451 .000 Freshman classification -1.226 .295 -.114*** ^.163 .000 Entering Freshman classification -1.021 .268 -.105*** -3.814 .000 Financial support from interest from .872 .272 .088** 3.204 .001 savings/investments Financial support from job -.585 .164 -.099*** -3.560 .000 Financial support from credit cards/ .839 .216 .108*** 3.880 .000 cash advances Financial support from financial aid -.438 .156 -.079* -2.799 .005 Human Sciences majors -.390 .160 -.067* -2.438 .015 R2 = .074 F= 12.631*** 2* = <.05 £»** = <.01 Q*** = <.001 116 Credit Card Purchases-Housing Expenses/Cash Advances As shown in Table 4.26, six socio-demographic variables were retained in the regression model. Ethnic background and financial support from credit cards/cash advances were the most significant variables in explaining students' use of credit cards for housing expenses/cash advances (p < .001). However, ethnicity had a negative relationship with housing expenses/cash advances, thereby suggesting that whites were less likely to use credit cards for housing expenses/cash advances than the members of the minority groups. Also showing a negative relationship, but at a lower level of significance (p < .01), were the gender and grade point average variables. Thus, a greater number of males as well as those with lower grade point averages were apt to use credit cards for housing expenses/cash advances. The regression results also showed that sophomore classification and parents' use of credit cards were significantly related to students' credit card use for housing expenses/cash advances at the .05 level. These six socio-demographic variables explained only 6% of the variance of the dependent variable. Thus, the R square indicates that there were additional factors that explained students' use of credit cards for housing expenses/cash advances. 117 Table 4.26. Regression Results with Socio-Demographic Characteristics as the Independent Variables and College Students' Credit Card Purchases (Housing Expenses/Cash Advances) as the Dependent Variable (N =1275). Variable b SE Beta t Sig. Constant 5.947 .319 18.643 .000 Ethnic background (white) -.566 .117 -.132*** -4.828 .000 Financial support from credit cards/ .600 .119 .137*** 5.027 .000 cash advances Gender (female) -.290 .083 -.097** -3.475 .001 Grade Point Average -.308 .091 -.094** -3.383 .001 Sophomore classification .286 .115 .068* 2.499 .013 Parents' use of credit cards .099 .046 .059* 2.140 .033 R^ = .063 F = 14.293*** e* = <.05 e** = <.01 2*** = <.001 Number of Late Payments Table 4.27 shows an R square with 8 socio-demographic characteristics that explained 13.7% of the variance of the dependent variable, number of late payments. The R square suggests the existence of additional factors that explain the variance of number of late payments. Grade point average, parents' credit-related problems, and senior classification were the most significant variables in explaining number of late payments made by students. These variables were all significant at the .001 level. However, grade point average 118 Table 4.27. Regression Results with Socio-Demographic Characteristics as the Independent Variables and Number of Late Payments as the Dependent Variable (N = 1270). Variable b SE Beta t Sig. Constant 2.035 .124 16.472 .000 Grade Point Average -.249 .031 -.209*** -7.920 .000 Parents' credit-related problems .101 .017 .162*** 6.064 .000 Senior classification .170 .036 .151*** 4.717 .000 Never married -.153 .049 -.083** -3.100 .002 Junior classification .102 .037 .088** 2.772 .006 Ethnic background (white) -.131 .042 -.084** -3.154 .002 Human Sciences majors .078 .032 .065* 2.441 .015 Financial support from credit cards/ .101 .043 .063* 2.368 .018 cash advances R^=.137 F = 25.030** e* = <.05 e** = <.oi 2*** = <.001 showed a negative relationship with students' number of late payments. Thus, students with a higher grade point average showed a greater propensity to make fewer late payments than did students with a lower GPA. On the other hand, students whose parents had experienced credit-related problems and students who were seniors were most likely to have higher levels of late payments on their 119 credit card bills. Being a Human Sciences major was less significant in explaining students' late credit card payments (p < .05). Three variables were significant at the .01 level of significance: never married, junior classification, and ethnic background. Juniors were more likely to make late payments on their credit card bills than were sophomores, freshmen, and entering freshmen. The never married and ethnic background variables had a negative relationship with the dependent variable that suggests students who were white and had never been married were less likely to be late with their credit card payments. Question 9 What is the relationship between college students' credit card knowledge and attitudes and credit card practices (reasons for using credit cards, number of cards, types of purchases, repayment practices)? Stepwise multiple regression analyses were used to explain college students' knowledge and attitudes as they relate to credit card practices. To aid in the regression analyses, factor analyses were conducted to reduce the number of variables measuring reasons why college students use credit cards and the types of items purchased with credit cards (Tables 3.2 and 3.3). Table 3.2 shows the results of the factor analysis for Questionnaire Item #13, reasons for college students' use of credit cards. Three factors, Convenience/Incentives, Support for Lifestyle/Education, and Emergency/Credit Record, were identified, each with loadings of .50 or higher. Table 3.3 shows the results of the factor 120 analysis for Questionnaire Item #7, types of credit card purchases made by college students. Three factors, Food/Clothing/Leisure, Education/Emergency, and Housing/Cash Advances, were identified. The loadings of each factor were .50 or greater. In addition to factor extractions, the dependent variables consisted of continuous data. The throughput/mediating variables, college students' attitudes and knowledge, were the independent variables in the analysis. As explained in Chapter Three, two attitude indexes were computed: an index of attitudes (unhealthy/healthy) toward use and view of credit cards and an index of attitudes (positive/negative) toward acquiring and using credit cards. In addition, an index of knowledge scores was computed. Cases with missing variables were excluded on a pairwise basis; in other words when subjects had missing variables, their data were excluded only for the calculations involving the missing variable. Reasons for Using Credit Cards-Convenience/Incentives Both attitude indexes were significant in explaining convenience/incentives as a reason for college students' use of credit cards. The results are shown in Table 4.28. Students who had unhealthy attitudes toward the use and view of credit cards, as well as those who had positive attitudes toward the acquisition and use of credit cards, were more likely to use them for convenience/incentives. The two independent/mediating variables explained 14.5% of the variance of the 121 use credit cards for emergencies/credit record. The two independent variables explained 5.5% of the variance of the dependent variable. This indicates that there were additional factors that explained the variance for credit card use in emergency/credit record. Table 4.30. Regression Results with Knowledge and Attitudes as the Independent Variables and College Students' Reasons for Using Credit Cards (Emergency/Credit Record) as the Dependent Variable (N = 1474). Variable b SE Beta t Sig. Constant 3.160 .310 10.200 .000 Positive attitudes toward credit card .089 .011 .205*** 8.072 .000 acquisition and use Knowledge .077 .017 .117*** 4.633 .000 R2= .055 F = 42.717*** B* = <.05 E** = <.01 2*** = <.001 Number of Credit Cards As shown in Table 4.31, positive attitudes toward credit card acquisition and use was the most significant variable in explaining the number of credit cards held by college students (e < .001). Students' credit card knowledge (g < .01) also had a significant relationship with the number of credit cards held by college students. These two variables explained 4.7% of the variance, and so the R 124 square suggests that there were additional factors that explained the number of credit cards which students possessed. Table 4.31. Regression Results with Knowledge and Attitudes as Independent Variables and Number of Credit Cards as Dependent Variable (N = 1464). Variable SE Sig. ic r Beta t Constant .001 .338 .004 .997 Positive attitudes toward credit card .070 .009 .196*** 7.693 .000 acquisition and use Knowledge .068 .020 .088** 3.464 .001 R=^ == .047 F = 35.942*' B* == <.05 B** = <.01 g*** = <.ooi College Students' Credit Card Purchases—Food/ Clothing/Leisure The three throughput/mediating variables representing college students' credit card attitudes and knowledge were significant in explaining food/clothing/ leisure expenses as a type of credit card purchases. As shown in Table 4.32, positive attitudes toward acquiring and using credit cards and unhealthy attitudes toward use and view of credit cards represent the two most significant variables (2 < .001). The third variable, knowledge, was less significant at the .05 level. Collectively, the three variables represent 15.3% of the variance of the dependent variable. Therefore, the R square suggests the existence of 125 additional factors that explained the variance of food/clothing/leisure expenses as a type of college students' credit card purchases. Table 4.32. Regression Results with Knowledge and Attitudes as the Independent Variables and College Students' Credit Card Purchases (Food/ Clothing/Leisure) as the Dependent Variable (N = 1476). Variable b SE Beta t Sig. Constant -1.619 .956 -1.694 .090 Positive attitudes toward credit card .407 .027 .361*** 14.828 .000 acquisition and use Unhealthy attitudes toward credit card .172 .019 .217*** 8.919 .000 use and view Knowledge .097 .041 .057** 2.381 .017 R^ = .153 F = 89.027*** e*:= <.05 p** = <.01 2*** = <.001 College Students' Credit Card Purchases—Education/ Emergency Only one throughput/mediating variable was retained by the regression model. Table 4.33 shows the regression results, with college students' positive attitudes toward acquiring and using credit cards as the independent variable and education/emergency expenses as the dependent variable. The independent variable was significant at the .001 level; however, only 3.1% of the variance of the dependent variable was identified. Therefore, the R square suggests that 126 additional factors explained the variance of education/emergency expenses as a type of college students' credit card purchases. Table 4.33. Regression Results with Knowledge and Attitudes as the Independent Variables and College Students' Credit Card Purchases (Education/ Emergency) as the Dependent Variable (N = 1474). Variable b SE Beta t Sig. Constant 4.207 .368 11.432 .000 Positive attitudes toward credit card .121 .018 .175*** 6.816 .000 acquisition and use R=^ = .031 F = 46.455*** e* = <.05 Q** = <.oi Q*** = <.001 College Students' Credit Card Purchases-Housing/ Cash Advances The three throughput/mediating variables in the regression analysis showed significance with the dependent variable, credit card housing expenses/ cash advances. Table 4.34 shows that two variables, unhealthy attitudes toward using and viewing credit cards and positive attitudes toward acquiring and using credit cards, were the most significant at the .001 level in explaining college students' credit card housing expenses/cash advance purchases. The knowledge variable was found to be less significant at the .05 level. This variable had a negative relationship with the dependent variable, which suggests that students with a lesser knowledge of credit cards had a greater propensity to use 127 credit cards for housing related/cash advance expenses. Only 3.9% of the variance was explained by the three independent variables, and the R square indicates that additional factors explained college students' housing related/cash advance purchases. Table 4.34. Regression Results with Knowledge and Attitudes as the Independent Variables and College Students' Credit Card Purchases (Housing Expenses/Cash Advances) as the Dependent Variable (N = 1473). Variable SE lo - Beta t Sig. Constant 3.325 .355 9.372 .000 Unhealthy attitudes toward credit card .048 .007 "174*** 6.720 .000 use and view Positive attitudes toward credit card .037 .010 .095*** 3.670 .000 acquisition and use Knowledge -.045 .015 -.075** -2.945 .003 R2=.03R2 = .0399 F = 20.079*** e* = <.05 B** = <.01 2*** = <.001 Number of Late Payments There were two throughput/mediating variables identified in the regression results that showed significance with the dependent variable, number of late payments. Table 4.35 shows that one of these variables, unhealthy attitudes toward use and view of credit cards, had a positive relationship. The other variable, positive attitudes toward acquiring and using credit cards, had a 128 negative relationship. This indicates that students with positive attitudes toward acquiring and using credit cards were less likely to make late payments on their credit card balances than students who had negative attitudes. Both variables were significant at the .001 level. Eleven percent of the variance of the dependent variable was explained by the two independent variables. Thus, the R square suggests that additional factors explained the number of late payments on credit card balances by college students. Table 4.35. Regression Analysis with Knowledge and Attitudes as the Independent Variables and Number of Late Payments as the Dependent Variable (N=1471). Variable b SE Beta t Sig. Constant 1.157 .106 10.948 .000 Unhealthy attitudes toward credit card .025 .003 .251*** 10.040 .000 use and view Positive attitudes toward credit card -.025 .004 174*** -6.957 .000 acquisition and use R2=.108 F = 89.098*** E* = <.05 Q** = <.01 *** _ < 001 Path Analytic Model The hypothesized conceptual model (Figure 3.1) based on the Deacon and Firebaugh theory of family resource management and on the credit card literature, was tested by a path analysis using Analysis of Moment Structures 129 (AMOS). This procedure was chosen because it allows the researcher to investigate relationships among variables. Because of the large number of variables identified in Research Questions 7-9, described in this Chapter, refinement had to be made and several variables needed to be deleted before the path analysis could be conducted. As shown in Figure 4.1, the four socio- demographic or predictor variables retained in the proposed model for the path analysis were age, gender, year (college classification), and grade point average (GPA). Age, year, and GPA were continuous variables, while gender was dummy coded. The throughput/mediating variables were retained without modification. These variables consisted of an index of knowledge scores as well as two attitude scales. Attitudes toward use and view of credit cards was labeled, "Unhealthy Attitude," while positive attitudes toward acquisition and use of credit cards was labeled, "Positive Attitude." Two output variables were retained for the model from among the variables describing credit card practices. These were credit card usage and total debt. An index of credit card usage was computed by summing the scores for Question 7 on the questionnaire, which measured students' level of credit card usage for selected items, such as food and clothing. An index of total debt was computed by summing the scores for bank card, store card, and gas card debt. 130 CO (0 V F l = .99 8 MSE A = . F l = .99 9 '(11df ) = 8 X ti xztrozo: •a o T3 c c (U tn •o (0 co" (D > J) (U o X m ^ .£3 C (0 c > (/) I o c CA E o g) 9- £= (0 C3) O CO O JC E CO CO 45 "' T3 >> c 0) (0 (0 •o (0 c CO a> < X CD 0) _Q . Q I "D 75 CO o o H O _ Q_ <0 do proposed relationship between the two output variables, usage and total debt. Although this was not in the original conceptual model, it was hypothesized that the path analysis would indicate that usage influences total debt. Correlation coefficients were provided for the socio-demographic variables, and goodness- of-fit indexes were applied to test the fit of the proposed model. In the model (Figure 4.1) the chi-square value was statistically significant at the .001 level, indicating a poor fit. However, with such a large sample size (N = 2,113), this was expected since the larger the sample size, the more likely it is that the model will be rejected (Arbuckle, 1997). In keeping with standard research practices, alternative goodness-of-fit methods were used to test the model. The results show that the root mean square error of approximation (RMSEA) was less than .08, thus indicating the model's fit was adequate. Also, the normed fit index (NFI) was greater than .90. Tile NFI usually varies from 0 to 1 with 1 being a perfect fit (Arbuckle). Similarly, according to Arbuckle, the comparative fit index (CFI) varies from 0-1 with values above .90 indicating an acceptable fit. The CFI in this path analysis was greater than .90. Finally the Tucker-Lewis coefficient, also called the Bentler-Bonnett non-normed fit index (NNFI), was applied. Again the model proved to be an acceptable fit with values close to 1. Therefore, it was concluded that the model does fit the data adequately and may be considered a 'plausible explanation" of the relations among the variables in the population (MacCallum, Roznowski & 132 Necowitz (1992, p. 490). As shown in Figure 3.1, the path between the two output variables, usage and total debt, was not in the hypothesized model. However, an intuitive case was made for exploring this relationship. It was found to be significant at the .01 level. An intuitive case also was made for running another model, with the mediating variables divided to provide an extra path between the socio- demographic variables and the knowledge and attitude variables. This second model is also supported by the Deacon and Firebaugh (1988) conceptual framework in which throughput is divided into the personal subsystem and the managerial subsystem (see Chapter II, Figure 2.1). For this study, it was hypothesized that knowledge would influence attitudes. The alternative model is similar to the first model in that all variables have been retained and correlation coefficients provided for the socio-demographic variables (Figure 4.2). In the alternative model, the paths between the socio-demographic variables and Knowledge are all statistically significant at the .001 level. Similarly, the paths between Knowledge and Positive Attitude and between Knowledge and Unhealthy Attitude are significant in a negative direction at the .001 level. Goodness-of-fit tests were applied to determine whether this revised model would be supported by the data. The large sample size again produced a Chi square value that was statistically significant with a probability level of <.001, 133 (U o « TJ ^4-^ D CD in. . CD tti t 9? o v O Q. nd A m * c/> CO (1)" 5 n) O (D le d < § T1 TJ o 0) c c TJ CD ^^ CD CO CD CD X O> CD >. SI riables , c CD CO c > 5 _o l_ o o CO CD CD grap h how n o CO CO b ro E n> ^-^ i- Q (D o 'o O o it ^ CD CD i/uJ O CD O) o 3 i_ sz TJ CD 'co in k (D _i n TJ CC 0) CD TJ N J2 O TI $ ^ CD o TJ C < CD lysi s -a CD C/5 T3 r e 4.2 . CD 3 05 o ?5 U. jj CO V 134 suggesting that the model should be rejected. However, the NFI was acceptable because the value exceeded .95, while the RMSEA value at .05 suggests that the model is a "good fit" (Kenny, 2003). The CFI at .998 indicates a very good fit, and the NNFI at .995 also suggests a very good fit. These values suggest that the alternative model may be a slightly better fit than the first model. Summary This chapter provided details of the data analysis and presented the findings of the study, which investigated the relationship between college students' socio-demographic characteristics (input variables), college students' credit card knowledge and attitudes (throughput/mediating variables), and college students' credit card practices (output variables). Participants in the study consisted of 2,113 undergraduate students enrolled at TTU in the fall semester, 2003. Statistical measures used to answer the research questions included descriptive statistics and stepwise multiple regression analyses which excluded cases with missing data on a painA/ise basis. A path analysis was used to test the conceptual model. The following were the major findings in the study: 1. Parents and, to a lesser extent, information in the mail were the major influences on college students' decisions to apply for their first credit card. Most students reported that the following were not at all influential: contacted by telemarketer, advertisement in newspapers/magazines, advertisement on the Internet, and advertisement on radio/television. 135 2. To establish a credit record, to cover emergency needs, and convenience were the most important reasons why college students used credit cards. 3. A third of the students holding credit cards used them often. The goods and services for which the cards were used most frequently were textbooks/school supplies, eating out, clothing, and groceries. Insurance, utility/ telephone bills, and rent/mortgage were paid for the least frequently with credit cards. 4. College students used cash advances to pay for entertainment-related expenses as well as groceries/household items. Insurance, housing expenses, and payment for another credit card bill were the items least likely to have been paid for with cash advances. 5. Parents were the primary source of financial knowledge for an ovenA/helming majority of college students. 6. A third of students had taken a course which included personal financial management principles. Among these, over 70 percent cited college as the institution where the course had been taken and economics and business as the courses in which the principles had been taught. 7. Students had generally favorable attitudes to on-campus financial management and financial counseling, but they were not as favorable to seeking advice from a counselor on campus. 136 8. A majority of students either had no credit card (bank, store, and gas cards) debt, or owed less than $1,000. Eight percent owed more than $3,000, with the highest reported total balance being more than $40,000. 9. More than 60 percent of the students surveyed had between $0 and $1,000 in consumer loan debt. Thirteen percent had a consumer loan balance of $7,000 or more. More than 7% did not know the amount of their consumer loan debt. 10. More than half the students surveyed had no student loan debt. Twenty-three percent owed $7,000 and over, while 7.5% were not sure what they owed. 11. Parents' income, financial support from job/scholarships, and being a business major were statistically significant in explaining college students' level of credit card knowledge. Students who were in the entering freshman, freshman, and sophomore classes appeared to have lower levels of credit card knowledge. 12. The variables that best explained an unhealthy attitude toward the use and view of credit cards were financial support from credit cards/cash advances and parents' credit-related problems. A high grade point average, parents' income, and father's level of education were significant in explaining a healthy attitude toward use and view of credit cards. 13. Several variables were significant in explaining a positive attitude toward acquisition and use of credit cards. These included parents' use of credit 137 cards, students' marital status (never married), students' major (business), and students' source of income (financial support from credit cards/cash advances). Having parents who had experienced credit-related problems was predictive of a negative attitude toward acquisition and use of credit cards. 14. The best predictors of students using credit cards for convenience/ incentives are having parents with high incomes, who used credit cards often when the students were growing up in the home. 15. Parents' credit related problems and students' financial support from credit cards/cash advances were the most likely variables to predict students' use of credit cards for support for lifestyle/college education. The higher the students' grade point average, the less likely students were to use credit cards to support their lifestyle and college education needs. 16. The higher the levels of financial support from savings and financial aid, and the greater the parents' use of credit cards, the more likely students were to use credit cards for emergency/credit record purposes. 17. Students' income level and gender (female) were the best predictors of the number of credit cards students possessed. Financial support from credit cards/cash advances, senior classification, parents' credit related problems, and students' hours of employment were weaker predictors. Whites were less likely to have a large number of credit cards when compared to members of other ethnic groups. 138 18. Parents' use of credit cards, students' financial support from credit cards/cash advances, and parents' income levels were the variables most likely to predict students' credit card food/clothing/leisure purchases. Students whose parents had the highest levels of credit-related problems were the least likely to use credit cards for food/clothing/leisure purchases. 19. The best predictors of students' use of credit cards for education/ emergency purposes were their parents' use of credit cards and students' financial support from credit cards/cash advances. Being an entering freshman or a freshman, and receiving financial support from a job were negatively related to using credit cards for education/emergency purposes. 20. Receiving financial support from credit cards/cash advances was a predicator of students' use of credit cards for housing expenses/cash advances. White females with a high grade point average were less likely to use credit cards for housing expenses/cash advances than were males, members of the minority groups, and students with a lower grade point average. 21. Students who make late payments on their credit card bills are likely to be seniors or juniors, who had observed their parents having credit-related problems. White students, who had never been married, and who had a high grade point average were less likely to make late payments on their credit card bills than married minority students whose GPA is low. 22. Students with unhealthy attitudes toward the use and view of credit cards and those with positive attitudes toward the acquisition and use of credit 139 cards were likely to use them for convenience/incentives. In addition, both attitude variables were significant in explaining students' use of credit cards to support lifestyle/college education. 23. Having an unhealthy attitude toward the use and view of credit cards and having a high credit card knowledge score were significant in explaining college students' use of credit cards for emergency/credit report purposes. 24. Students who had a positive attitude toward the acquisition and use of credit cards and those who had a high credit card knowledge score were most likely to have a greater number of credit cards. 25. Having a positive attitude toward the use of credit cards, possessing an unhealthy attitude toward credit card use and view, and achieving a high score on the credit card knowledge scale were significant in explaining college students' use of credit cards to pay for food/clothing/leisure goods and services. 26. Students who had a positive attitude toward acquiring and using credit cards were likely to use credit cards for education/emergency purposes. 27. Students who had an unhealthy attitude toward the use and view of credit cards, those with a positive attitude toward acquiring and using credit cards, and students with a low level of credit card knowledge were likely to use their cards for housing expenses/cash advances. 28. Students who had an unhealthy attitude toward the use and view of credit cards were likely to pay their credit card bills late, while students with a 140 positive attitude toward the acquisition and use of credit cards were less likely to make late payments. 29. The variances were limited in all equations, with R^ ranging from 2.5% to 23%. This suggested that there were other factors, in addition to those variables found to be significant, contributing to the variances of the dependent variables in all equations. 30. A path analysis was used to test the conceptual model. Age, gender, college classification, and grade point average were the socio-demographic variables. An index of two attitude scores and an index of knowledge scores were the mediating variables, and an index of credit card usage and college students' total credit card debt were the output variables. Goodness-of-fit indexes were applied to the data, and they revealed that the model was an adequate fit for the data. 31. A second path analysis was run, using knowledge as a path between the input variables and the mediating variables. There was statistical significance between the input variables and knowledge. In addition, there was statistical significance between knowledge and both attitude scales in a negative direction. The alternative model was also a good fit for the data. 141 CHAPTER V SUMMARY, FINDINGS, CONCLUSIONS, AND RECOMMENDATIONS This chapter provides a summary of the study and its findings. Limitations of the study, implications for educational programming, and recommendations for future research also are provided. Summary of the Study The primary purpose of the study was to provide data on the credit card knowledge, attitudes, and practices of undergraduate students enrolled at TTU. In addition, the research endeavor sought to add to the body of literature by utilizing a revised theoretical framework of the Deacon and Firebaugh (1988) model of family resource management. The Deacon and Firebaugh conceptual model posits three components: input, throughput, and output. In the study, relationships among college students' socio-demographic background (input), students' credit card knowledge and credit card attitudes (throughput), and students' credit card practices (output) were investigated. The study specifically examined the following research questions: 1. How did college students acquire their credit cards? 2. What reasons do students give for using credit cards? 142 3. What types of purchases do students make most frequently with credit cards? 4. Where do college students obtain their knowledge of personal finance/money management principles? 5. What are college students' attitudes toward financial education and counseling? 6. What is the debt level of college students as it relates to: a. credit card debt b. student loan debt c. other consumer debt? 7. What is the relationship between selected socio-demographic variables (age, gender, ethnicity, marital status, classification, major, grade point average, employment, income, sources of income, and socioeconomic background) and a. college students' credit card knowledge b. college students' credit card attitudes? 8. What is the relationship between selected socio-demographic variables (age, gender, ethnicity, marital status, classification, major, grade point average, employment, income, sources of income, and socioeconomic background) and college students' credit card practices (reasons for using credit cards, number of cards, types of purchases, repayment practices)? 143 9. What is the relationship between: a. college students' credit card knowledge b. college students' credit card attitudes and college students' credit card practices (reasons for using credit cards, number of cards, types of purchases, repayment practices)? The widespread use of credit cards by college students has raised questions about students' ability to effectively manage their finances. This has led, in recent years, to several studies focusing on college students and credit card practices. Several researchers have examined the extent of credit card usage by students and have concluded that the majority of college students use credit cards, with many carrying substantial balances each month. At TTU, anecdotal evidence suggested that some students were struggling with debt and the inability to manage their finances effectively. To address this problem, a multi-organizational program. Red to Black, was established to provide financial education and counseling for TTU students. However, a better understanding of students' credit card knowledge, attitudes, and practices was needed, and this study sought to provide such information. Current data on college students' credit card knowledge, attitudes, and practices will provide college administrators and counselors with a clearer understanding of the financial education needs of the TTU student population. This will assist 144 them in determining the best practices to equip college students with the knowledge and skills to exercise responsible financial behavior. The instrument used in the study was developed by the researcher after a literature search revealed no single existing instrument suitable for the study. A review of questionnaires used in previous studies on college students and credit cards, as well as personal communication with researchers, yielded concepts and questions for the development of a suitable instrument. The questionnaire was reviewed by a panel of experts. It was pilot-tested with 30 entering freshmen enrolled in an orientation course and 49 juniors and seniors enrolled in a capstone course in the College of Human Sciences at the beginning of the fall 2003 semester. The final version of the questionnaire, "College Student Credit Card Survey," consisted of 39 questions measuring college students' credit card knowledge, attitudes, and practices, as well as students' socio-demographic characteristics. The study was approved by the TTU Committee for the Protection of Human Subjects before data were collected. The sample for the study (N = 2,113) consisted of undergraduate students enrolled at TTU in the fall 2003 semester. The sample represented all nine colleges. The survey was administered either by the researcher or the class instructor to a convenience sample. The students completed the survey in twenty to twenty-five minutes of class time. Data collection occurred over a seven-week period during October and November 2003. 145 Data were analyzed using the Statistical Package for Social Sciences (SPSS) and Analysis of Moment Structures (AMOS). Descriptive statistics, including frequencies and means, were used to examine student responses to the input variables (socio-demographic characteristics), throughput or mediating variables (credit card attitudes and knowledge), and output variables (credit card practices). Descriptive statistics were also applied to determine how participants acquired credit cards, their reasons for using credit cards, and the types of purchases made with credit cards. In addition, descriptive statistics identified where students had obtained their knowledge of personal finance/ money management principles; college students' attitudes toward financial education and counseling; and college students' credit card, student loan, and other consumer debt. Stepwise multiple regression analyses were conducted to answer Research Questions 7 to 9. These questions examined the relationships between the following: college students' socio-demographic characteristics and college students' credit card knowledge and attitudes (input to throughput variables); college students' socio-demographic characteristics and college students' credit card practices (input to output variables) and between college students' credit card knowledge and attitudes and college students' credit card practices (throughput to output variables). College students' attitudes were measured by two questionnaire items. An index of unhealthy/healthy attitudes toward use and view of credit cards was 146 computed for Question 14 (see Appendix A), which was answered by only those students who held at least one credit card. Descriptive statistics revealed an item mean of 2.30 on a scale ranging from 4 = Strongly Agree (Unhealthy Attitude) to 1 = Strongly Disagree (Healthy Attitude). An index of college students' positive/negative attitudes toward acquiring and using credit cards was computed for Question 16 (see Appendix A), which was answered by all students. The item mean was 2.26 on a scale ranging from 4 = Strongly Agree (Positive Attitude) to 1 = Strongly Disagree (Negative Attitude). Finally, path analyses with goodness-of-fit indexes were used to test whether the Deacon and Firebaugh conceptual model would fit the data. An alpha level of .05 was used to determine statistical significance. Summary of the Findings Socio-Demographic and Background Information The sample included undergraduate students enrolled in the fall 2003 semester at TTU. Of the 2,113 respondents, 48.3% were male and 51.7% female. The majority of the students were white, under the age of 24, and had never been married. Seventy percent of the students were upper classmen Ouniors and seniors), and 60% represented two colleges (Business and Human Sciences). The majority of respondents (95%) reported having a grade point average exceeding 2.49. Although not drawn from a random sample, the respondents in the study were fairly representative of the student population at 147 TTU. Data retrieved from the Texas Tech Institutional Research and Information Management Web site (April 20, 2004), indicated that the population for the fall 2003 semester consisted of 54.6% male and 45.4% female, with 92.2% of the students being under the age of 24. Eighty-two percent of the population was white. The minority groups included Hispanic (11%), Black (3.1%), Asian (2.2%), and American Indian (0.6%). Respondents were from middle- to high-income households with 55% of the students reporting that their parents' annual income was over $75,000 and 38% reporting an annual household income of more than $100,000. In addition, more than 80% of the students reported that their parents had reached a level of education beyond that of a high school diploma, with approximately 20% having received an advanced degree. Given the mean age (21) of the respondents and the socioeconomic level of the household, it is not surprising that financial support from parents was the leading source of income. The second highest source of income was employment, with approximately half the respondents working between 11 and 40 hours a week. In reporting their own annual income, 48% of the students indicated that their income level was less than $6,000, while 23.6% reported an income of $6,000 to $11,999. Although students were asked to include total income, including parental support, it is possible that those indicating very low levels of income may have reported only actual monies received. Students may not have 148 taken into account parental support in the form of payments made for college tuition and room and board. More than 60% of the students reported that their parents used credit cards often or very often, while 55% indicated that parents had never experienced credit related problems. Of the 1,486 (70.3%) students who reported holding a credit card, 36% indicated having at least one credit card, while14.3% held five or more cards, and 12 students reported having more than 10 cards. Almost half of the respondents had received their first credit card prior to entering college, while one third of the students had obtained their first card during their first year of college. Research Questions The first research question identified how college students had acquired their credit cards. Descriptive statistics revealed that the most influential factors were parents' suggestions (57.2%) and receiving information through the mail (31%). In contrast, most respondents indicated that telemarketing (93.1%), newspaper/magazine advertisements (92%), radio/ television advertisements (90.8%) and Internet marketing (90.8%) had no influence on their decision to acquire a credit card. Research question #2 examined reasons why college students use credit cards. Establishing a credit record (71.6%), emergencies (66.8%), and convenience (63.6%) were the reasons cited by college students as being most 149 important. Reasons reported not to be at all important were keeping up with friends (87.9%), cash advances (82.1%), and borrowing for college (76.6%). The third research question examined college students' credit card purchases. Textbooks and school supplies (61.4%), eating out (58.6%), and clothing (56.4%) were the items purchased most often with credit cards. Most students indicated that they never or hardly ever used credit cards to purchase or pay for rent/mortgage (97.7%), utilities/phone (92.9%), or cash advances (90.3%). Students who acknowledged that they had used their credit cards to obtain cash advances, cited entertainment (46.6%) and groceries (28.2%) as the two items paid for most often with cash advances. Items for which cash advances were used the least were insurance (0.4%) rent/mortgage (7.1%), and payment for another card (7.1 %). Results for Research Question #4 indicated that of the 726 students (34.5%) who had taken a course in which personal financial management principles were taught, 71.3% cited college as the place where they had taken the course (71.3%). Economics (61.8%) and business (52.4%) were cited most often as the course in which personal financial principles were taught. However, when asked to indicate their primary source of financial knowledge, most students did not cite the course they had taken, but rather parents (83.5%). Only 6.3% of the students cited a course in high school or college as their main source of financial knowledge. The Internet (0.6%) was the least important source of financial knowledge for college students. 150 A four-item scale was used to answer Research Question #5 and identify students' attitudes toward financial education and counseling. While 90.9% of the students agreed or strongly agreed that financial counseling should be available for all students on campus, only 48% reported that they would personally use such a service. Seventy percent (N = 1462) of the students believed that taking a course in personal financial management would help them better manage their finances; however, only 54.5% agreed that all freshmen should be required to take a course in personal financial management. The sixth research question examined the debt level of college students as it relates to credit card debt, consumer debt, and student loan debt. Bank card, store card, and gas card balances were summed to produce a total credit card debt level for college students. More than 75% of the students owed less than $1,001; however, 14% owed between $2,001 and $44,000. The average debt for these students was $1,036.08. There were 341 non-responses from among respondents who held at least one card, indicating either a lack of knowledge of their credit card balances, or an unwillingness to reveal the amount. When asked to indicate their consumer debt, almost 50% of the students surveyed indicated that they had no consumer debt (48.8%). Of the remaining students with consumer debt balances, 15.1% had balances totaling less than $1,000, while 13.4% had balances of over $7000. With regard to student loan debt, more than half the respondents reported that they had no student loans 151 (52.8%); however, almost 25% had a student loan debt totaling more than $7,000. Approximately 8% of the students answered, 'Tm not sure," when asked to report on their student loan debt and their consumer loan debt. The results of the multiple regression analyses used to answer Research Question 7 (what is the relationship between selected socio-demographic variables and college students' credit card knowledge and credit card attitudes?) pointed to the following: 1. Parents' income, financial support from job/scholarships, and being a business major were statistically significant in predicting college students' level of credit card knowledge. Students who were in the entering freshman, freshman, and sophomore classes appeared to have lower levels of credit card knowledge. 2. The variables that best explained an unhealthy attitude toward the use and view of credit cards were financial support from credit cards/cash advances and parents' credit-related problems. A high grade point average, parents' income, and father's level of education were significant in explaining a healthy attitude toward use and view of credit cards. 3. Several variables were significant in explaining a positive attitude toward acquisition and use of credit cards. These included parents' use of credit cards, students' marital status (never married), students' major (business), and students' source of income (financial support from credit cards/cash advances). Having parents who had experienced credit-related problems was predictive of a negative attitude toward acquisition and use of credit cards. 152 The results of the multiple regression analyses used to answer Research Question 8 (what is the relationship between selected socio-demographic variables and college students' credit card practices?) pointed to the following: 1. The best predictors of students using credit cards for convenience/ incentives were having parents with high incomes, and who used credit cards often when the students were growing up in the home. 2. Parents' credit related problems and students' financial support from credit cards/cash advances were the most likely variables to predict students' use of credit cards for support for lifestyle/college education. The higher the students' grade point average, the less likely students were to use credit cards to support their lifestyle and college education needs. 3. The higher the levels of financial support from savings and financial aid and the greater the parents' use of credit cards, the more likely students were to use credit cards for emergency/credit record purposes. 4. Students' income level and gender (female) were the best predictors of the number of credit cards students possessed. Financial support from credit cards/cash advances, senior classification, parents' credit related problems, and students' hours of employment were weaker predictors. Whites were less likely to have a large number of credit cards than students of other ethnic groups. 5. Parents' use of credit cards, students' financial support from credit cards/cash advances, and parents' income levels were the variables most likely to predict students' credit card food/clothing/leisure purchases. Students whose 153 parents had the highest levels of credit-related problems were the least likely to use credit cards for food/clothing/leisure purchases. 6. The best predictors of students' use of credit cards for education/ emergency purposes were their parents' use of credit cards and students' financial support from credit cards/cash advances. Being an entering freshman or a freshman, and receiving financial support from a job were negatively related to using credit cards for education/emergency purposes. I 7. Receiving financial support from credit cards/cash advances was a predicator of students' use of credit cards for housing expenses/cash advances. White females with a high grade point average were less likely to use credit cards for housing expenses/cash advances than were males, members of the minority groups, and students with a lower grade point average. 8. Students who made late payments on their credit card bills were likely to be juniors or seniors who had observed their parents having credit-related problems. White students, who had never been married, and who had a high grade point average were less likely to make late payments on their credit card bills than married minority students with low GPAs. The results of the multiple regression analyses used to answer Research Question 9 (what is the relationship between college students' credit card knowledge and credit card attitudes and college students' credit card practices?) revealed the following: 154 1. students with unhealthy attitudes toward the use and view of credit cards and those with positive attitudes toward the acquisition and use of credit cards were likely to use them for convenience/incentives. In addition, both attitude variables were significant in explaining students' use of credit cards to support lifestyle/college education. 2. Having an unhealthy attitude toward the use and view of credit cards and having a high credit card knowledge score were significant in explaining college students' use of credit cards for emergency/credit report purposes. 3. Students who had a positive attitude toward the acquisition and use of credit cards and those who had a high credit card knowledge score were most likely to have a greater number of credit cards. 4. Having a positive attitude toward the use of credit cards, possessing an unhealthy attitude toward using and viewing credit cards, and achieving a high score on the credit card knowledge scale were significant in explaining college students' use of credit cards to pay for food/clothing/leisure goods and services. 5. Students who had a positive attitude toward acquiring and using credit cards were likely to use credit cards for education/emergency purposes. 6. Students who had an unhealthy attitude toward the use and view of credit cards, those with a positive attitude toward acquiring and using credit cards, and students with a low level of credit card knowledge were likely to use their cards for housing expenses/cash advances. 155 7. Students who had an unhealthy attitude toward using and viewing credit cards were likely to pay their credit card bills late, while students with a positive attitude toward the acquisition and use of credit cards were more likely to make credit card payments on time. The results of the stepwise multiple regression analyses indicated that the variances were limited in all equations, with R^ ranging from 2.5% to 23%. This suggested that there were other factors, in addition to those variables found to be significant, contributing to the variances of the dependent variables in all equations. A path analysis was used to test the conceptual model. Age, gender, college classification, and grade point average were the socio-demographic variables. An index of two attitude scores and an index of knowledge scores were the mediating variables, and an index of credit card usage and college students' total credit card debt were the output variables. Goodness-of-fit indexes were applied to the data, and they revealed that the conceptual model was an adequate fit for the data. A second path analysis was run, using knowledge as a path between the socio-demographic variables and the mediating variables. There was statistical significance between the socio-demographic variables and knowledge. In addition, there was statistical significance in a negative direction between knowledge and the two attitude scales. Goodness-of-fit indexes revealed that this alternative model was also a good fit for the data. 156 Conclusions and Discussion The sample in the study included undergraduate students enrolled in the fall 2003 semester at TTU. The sample was almost evenly divided between male (48.3%) and female (51.7%). From the self-report study, the researcher concluded that the 2,113 respondents appeared to be a fairly homogeneous group. They were mainly white, under the age of 24, had never been married, and came from middle-to upper- level socio-economic households. Although not drawn from a random sample, the respondents in the study were fairly representative of the student population at TTU. The student population during the fall 2003 semester consisted of 54.6% male and 45.4% female, with 92.2% of the students being under the age of 24. Eighty-two percent of the population was white (Texas Tech Institutional Research and Information Management, 2003). Sixty percent of the students were from two colleges (Business and Human Sciences). While the sample was fairly representative of the age, gender, and ethnicity of the student population, one of the limitations of using a convenience sample was that some colleges were over-represented, while others were under-represented. This was particularly true of the Business, Human Sciences, and Arts and Sciences colleges. The majority of respondents had a grade point average exceeding 2.49, and most of them had highly educated parents. Most students indicated that their parents were in the middle-to upper- income bracket and were providing most of the students' financial support. More than 60% of the students reported 157 that their parents used credit cards often or very often, while 55% indicated that parents had never experienced credit related problems. Because the data were based on a self-report study, it is possible that these findings may be inaccurate. The researcher's assumption, however, is that it is an accurate reporting and entirely likely, given the high standard of living and the large numbers of professionals connected with the University and the medical field in Lubbock. Seventy percent of the students surveyed reported having at least one credit card. Of these, 14.3% held five or more cards, and 12 students reported having more than 10 cards. Almost half of the respondents had received their first credit card prior to entering college, while one third of the students had obtained their first card during their first year of college. These figures match the national average (Joo et al., 2003; Lyons, 2003). Parents were the greatest influence on students acquiring credit cards. A secondary influence was receiving information through the mail. Most students were not influenced by marketing techniques such as telemarketing and advertisements in/on newspapers, magazines, radio, television, the Internet, and campus promotions. These results were surprising in light of the billions of dollars the credit card issuers are reportedly spending to target college students. However, the results are consistent with a Student Monitor study, which reported that 36% of students acquired their cards by responding to offers in the mail (GAO, 2001). In addition, a 1998 study by TERI/IHEP revealed that mail 158 solicitation accounted for one-third of students' credit cards and that 63% of students applied for their cards on their own. Although students reported that they had acquired a credit card to establish a credit record and to use for emergencies and convenience, it appeared that in practice students used credit cards regularly to support their lifestyle. Purchasing textbooks/school supplies and clothing as well as eating out were the items for which college students used their credit cards most frequently. In addition, those students who had used their cards to obtain cash advances did so to pay for entertainment and to purchase groceries. This finding lends support to a study by Joo et al. (2003), which found that students acquired credit cards mainly for convenience and emergencies and used their credit cards for everyday expenses. Apparel and services, food away from home, entertainment, education, and groceries were among the items purchased most frequently with credit cards. More than one-third of the respondents had taken a course containing personal financial principles either in high school, college, or some other institution/organization. However, a very small percentage of the students reported that a course in college or high school was their primary source of financial knowledge. An ovenwhelming majority of the students identified parents as their primary source of financial information. These findings are consistent with the results of a 2001 study called "Parents, Youth, and Money" (ASEC), 159 which revealed that 94% of students received their financial education from parents. While the majority of students agreed or strongly agreed that financial counseling should be available to all students on campus, less than half reported that they would personally use such a service. It is possible that students who were not in financial difficulty at the time of the study could not envision ever needing the help of a financial counselor. According to Hayhoe et al., (1999), students apparently seek financial education and counseling in reaction to perceived or actual credit problems. The majority of students in this study had no outstanding debts, or kept their credit card, consumer, and student loan debt at a level below $1,000. In addition, most students paid their balances off in full each month, or paid more than the minimum due. However, several students had dangerously large debt levels, and too many students did not know how much money they owed. Approximately, 3% of the students were either juggling payments each month or were unable to make the minimum payments that were due. This finding supports the results of several studies which found that while most students are responsible users of credit cards, an increasing number of students may be financially at-risk because they are not paying their cards in full each month and may be accumulating large amounts of debt (Armstrong & Craven, 1993; Jamber-Joyner et al., 2000; Joo et al.,2003; Lyons, 2003; Nellie Mae, 2002). 160 Parents' income, financial support from job/scholarships, and being a business major were statistically significant in predicting college students' level of credit card knowledge. Students who were in the entering freshman, freshman, and sophomore classes appeared to have lower levels of credit card knowledge. However, overall, students were fairly knowledgeable about credit cards. This may be explained by the fact that a large number of business, agri-business, and personal financial planning majors completed the survey. It is not surprising, then, that this particular sample would be knowledgeable about the principles of credit card use. However, this knowledge did not necessarily translate into a practical, working knowledge as many students did not know their credit card balances, consumer loan debt, student loan debt, and the annual interest rates on their credit cards. Students who indicated that they worried when using credit cards, purchased items because they had an available credit card or because they felt depressed, and were delaying the repayment of credit card debt until they were out of school were considered to have an unhealthy attitude toward the use and view of credit cards The variables that best explained an unhealthy attitude toward the use and view of credit cards were financial support from credit cards/cash advances and parents' credit-related problems. Students who were unable to make ends meet were using credit cards and cash advances to supplement their income. It appears that they were actually following the examples set by parents who had not been good role models in fiscally 161 responsible behavior when students were growing up in the home. This suggests that there may be a connection between unhealthy attitudes toward the use and view of credit cards and unhealthy credit practices. Students' income level and gender (female) were the variables that best explained the number of credit cards students possessed. Financial support from credit cards/cash advances, senior classification, parents' credit related problems, and students' hours of employment were less significant variables. Whites were less likely to have a large number of credit cards when compared to members of other ethnic groups. These findings are not totally supported by those of Armstrong and Craven (1993), who found that being white and female were predictors of the number of credit cards a student owned. On the other hand. Manning (1999) concluded that socioeconomic background has a strong influence on the number of cards students possessed. Students who came from middle and upper income families, and whose parents were college educated, possessed more cards than did students from lower socioeconomic households. Limitations of the Study The data were based on a self-report study. Respondents may not have been honest and accurate in their answers because of the personal and sensitive nature of the study. Discussing personal financial matters is still considered taboo by many, and students may not have felt comfortable responding to questions about, for example, annual income, amount charged each month, and 162 debt level. In addition, some respondents indicated on the survey that they were not sure of their answers and were providing guesses for items such as annual percentage rates, credit card balances, and the level of student loan debt. The study was conducted during the middle of the fall semester. Results may have been distorted because of the following: Freshmen students entering college for the first time that academic year may not yet have acquired a credit card. Students who had already acquired a credit card may not have held it long enough to report accurately on their repayment practices. In addition, returning students may have paid off credit card balances from the previous year with income from their summer employment or gifts received from parents. A random sample was not used for this study because of financial and time constraints. The convenience sample was quite large and fairly representative of the population enrolled at TTU during the fall 2003 semester (as discussed earlier in this Chapter). However, some colleges, especially Arts and Sciences and Engineering, were under-represented. Others, particularly Business and Human Sciences, were over-represented. The results of this study may be generalized only to the sample which was surveyed. The large number of business, human science, and agri-business majors may have biased the results of the study because of the students' exposure to business and personal financial management principles. Result bias may have occurred as well because of students' socioeconomic background. A large 163 majority of students came from homes where parents were well educated and earned middle to upper levels of income. The design of the survey instrument contained minor flaws, which were identified during the period of data entry and analysis. Some of the survey items produced categorical data, which could not be used in the multiple regression analyses. While answering the question addressing students' primary source of financial knowledge, many respondents circled more than one answer. This resulted in loss of information because their responses were entered as missing data. The question measuring the influence different marketing techniques had on college students' acquiring credit cards was apparently misinterpreted by several students, who believed they were answering a question on the reasons why they use credit cards. Another limitation is the small cell frequencies in certain categories. For example, 1,794 of the respondents were white, while there were only four American Indians who completed the survey. For the purposes of the data analysis, it was necessary to collapse the categories into subcategories. African- Americans, Hispanics, Asians, and other minority groups were combined together as "all others." Similarly not married but living with partner, divorced, separated, and widowed were labeled "all others." 164 Implications The results of this study hold implications for family and consumer sciences educators and professionals. The family and consumer sciences profession is dedicated to empowering individuals, strengthening families, and enabling communities. It is concerned with the role of individuals and families as consumers of goods and services, and it practices the integration of knowledge across subject and functional areas (NASAFACS-V-TECS, 1998). The family and consumer sciences profession uses a systems approach to problems and is focused on providing prevention as well as intervention services. The family and consumer sciences professional is well-suited to meeting the financial education needs of students, by identifying financially at-risk students and equipping them with the skills to behave in a fiscally responsible manner. While the data indicated that most students managed their credit cards responsibly, that they were knowledgeable about credit cards, and paid their balances in full each month, the research also revealed that 188 (14.1%) of the students surveyed had credit card debt ranging from $2,001 to $44,000. In addition, several students paid either the minimum balance each month or tried to juggle their payments from one month to another. Twenty-three students (1.6%) were unable to pay the minimum balance. These findings suggest that there is a critical need for those students who are potentially at-risk to be identified and counseled concerning good credit practices. 165 students need to receive financial education on debt management as well as credit card management. It also will be critical for students to receive effective educ^ation and counseling on consumer issues. These education and counseling components need to focus on how consumer advertising may attract students to spend unnecessarily as students need to be taught how to avoid appealing consumer traps. Several institutions of higher education are beginning to address the problem. To date, six universities, including TTU "have either financial education or financial counseling provided in a peer to peer arrangement. Two other universities have financial counseling or financial education centers" (D. Bagwell, personal communication, April 21, 2004). Certainly, the Red to Black Center at TTU has developed, and will continue to develop, educational training and counseling components that will address these issues. Family and consumer sciences educators also may play a significant role as they prepare student teachers to teach family and consumer sciences. They can seek to impress on future teachers the importance of including personal financial management in their lesson plans. College students need to be educated in the areas of credit card behavior and debt management through various avenues. Because the research findings indicate that 52% of the students would not personally consult a credit counselor on campus, the classroom environment may be an alternative location to provide this critical information. Educators should have access to consumer education 166 workshops and other professional development opportunities that can provide them with the expertise to integrate credit card and debt management into their teaching specialty. At the middle and high school level, family and consumer sciences teachers can play a significant role in teaching financial management principles to students. Organizations such as the National Endowment for Financial Education, Cooperative Extension in several states, and Jump$tart have developed personal financial management curricula for children and youth. In addition, family and consumer sciences teachers have access to the National Standards and/or to family and consumer sciences curricula at the state level. Teachers may use these resources to develop financial management lesson plans to teach sound financial principles to students before they graduate from high school. Family and consumer sciences educators in schools, universities, and in community settings should be creative in utilizing different kinds of learning experiences and teaching methods to provide students with knowledge of financial principles. The findings of this study, and other research, indicated that students may prefer not to attend face-to-face counseling sessions, or they may not see the need for financial advice until credit becomes a problem. Using interactive learning experiences, on-line classes, videos, and CDs may be useful tools to transfer information in a less intimidating arena. In addition, family and consumer sciences educators should involve students in practical learning 167 experiences such as opening a savings account, budgeting, focus groups, and poverty simulations. Learning by experience may be far more effective teaching tools than merely imparting knowledge and theory. Family and consumer sciences professionals who deliver financial management programs to community members through Cooperative Extension may play a vital role in equipping students to handle finances responsibly. The research showed that students get most of their financial knowledge from parents. Also, parents can be good, or bad, role models in how to manage credit cards and other financial matters. County agents/field faculty and other Extension professionals have a unique opportunity to have an impact on the fiscal behavior of today's youth by equipping their parents and other adults in the community to be good role models, with the necessary skills to manage their finances well. As college education costs continue to rise, family and consumer sciences professionals need to be a strong and dependable resource to lower income families, who have children desiring to attend college. This may be achieved by providing young people and families with the knowledge and skills to access scholarships and other forms of financial aid. In this way, junior and high school teachers, extension professionals, and college professors would be helping to steer lower socioeconomic college students away from using credit cards to help finance their college education. 168 Recommendations for Further Research Results of the study indicate the need for further research. Specific recommendations are listed below: • The data from the existing study should continue to be analyzed, focusing on college students' credit card knowledge, attitudes, and practices as they relate to academic majors. Business, personal financial planning, and agri-business students should be compared with students from other majors, to determine whether majors in a finance-related field have higher levels of credit card knowledge, more healthy attitudes toward the use of credit cards, and lower levels of debt. • The survey instrument should be further refined by restructuring some questions to obtain continuous rather than categorical data. This will allow additional data to be included in the multiple regression analyses and the path analyses. • Items in the Positive/Negative attitude scale should be examined and modified to increase the reliability of the scale. More items from the favorable and unfavorable affective, cognitive, and behavioral attitude scale developed by Xiao et al. could be included, and a factor analysis conducted, to determine suitability of the items. • Building upon the four statements measuring college students' responses to financial counseling and education, an attitude scale should be 169 developed to measure college students' attitudes to financial counseling and financial educational programs on campus. • The study should be replicated with a random sample of TTU students so that the results may be generalized to the entire student body. • The study should be replicated using a random sample of non-traditional students, graduate students, and international students. Such a study should be more representative of the minority groups on campus as well as students from lower socioeconomic levels whose college education is not paid for by parents. • Both qualitative and quantitative measures should be used in a replication of this study. Focus groups and interviews could be utilized as a means of gaining additional insight into the credit card knowledge, attitudes, and practices of college students. • A longitudinal study of college students' credit card practices should be conducted to document the changes that occur over time, especially with regard to debt level. A group of students should be surveyed on entering college, during their last semester as a senior, two years after graduation and five years after graduation to determine the following: Does the debt level of college students increase as they progress through college? How much debt do these young adults have as they graduate from college and enter the workforce? 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Unpublished doctoral dissertation, Louisiana State University, Baton Rouge. Upton, A. L. (1992). College students' use and knowledge of credit cards. Unpublished master's thesis, Tennessee State University, Nashville. Vickers, M. (1999). Big cards on campus. Business Week, 3620,136-138. Wanvick, J., & Mansfield, P. (2000). Credit card consumers: college students' knowledge and attitude. The Journal of Consumer Marketing, 17(7), 617- 626. Xiao, J. J., Noring, F. E., & Anderson, J. G. (1995). College students' attitudes towards credit card. Journal of Consumer Studies and Home Economics, 19, 155-174. 177 APPENDIX B LETTER OF INTRODUCTION 178 Dear Faculty Member/Instructor: I am a doctoral candidate in the College of Human Sciences at Texas Tech University, and my dissertation topic is, "Credit Card Knowledge, Attitudes, and Practices of College Students." The purpose of the proposed study is to gain insight into the financial behaviors of the college student population. It also will provide practical information for administrators, educators, and counselors as they seek to meet the needs of students by developing and delivering educational programs and effective counseling approaches to address the financial challenges faced by undergraduate students. During the next two to three weeks, I would like to collect data from approximately 1500 Texas Tech students who will be asked to complete a College Student Credit Card Survey. I am writing to ask if you would give students in your course(s) permission to complete the survey during 20 minutes of your class time. I would greatly appreciate your assistance in distributing and collecting the surveys at a time that is convenient to you over the next two to three weeks. However, if you are unable or unwilling to do so, I would be happy to arrange to visit your class and administer the survey personally. The questionnaires will be available from Tuesday, October 7. Would you confirm by e-mail ([email protected]) or by telephone (470-1228), whether you wish to distribute the questionnaires personally or would prefer me to administer the survey at the beginning or end of one of your class sessions. I sincerely appreciate your support of this research project and would be happy to provide you with a summary of the research findings. Sincerely, Rosita Moore P.S.: Attached, please find a copy of the letter to the survey participants. 179 Dear Survey Participant: Thank you for taking the time to complete this important questionnaire on the financial practices of college students. All responses will remain strictly confidential. Your name will not be associated with either the response or the results Your participation in this study is completely voluntary, and you are free to refuse or discontinue participation at any time. Your decision to participate will not affect your present or future relations with Texas Tech University; however, by completing the survey, you will provide us with valuable information on how credit card usage is affecting today's college students. This research has been approved by the Institutional Review Board for Research involving Human Subjects at Texas Tech University. By completing this survey, you acknowledge your voluntary agreement to participate in this study. The questionnaire will take you approximately 15 to 20 minutes to complete. We appreciate your taking the time to fill out our survey. If you would like your name to be entered into a drawing for $100 In cash, please fill out the slip below and place it in the envelope/box provided. If you have any questions about this survey, please feel free to contact the principal investigators. Rosita Moore, M.A. Ginny Felstehausen, Ph.D. Sue Couch, Ph.D Doctoral Candidate Professor Professor Family & Consumer Sciences Ed. Family & Consumer Sc. Ed. Family & Cons Sc. Ed. College of Human Sciences College of Human Sciences College of Human Sciences Texas Tech University Texas Tech University Texas Tech University Phone: 470-1228 Phone: 742-3068 Phone: 742-3068 180 APPENDIX C LETTERS OF PERMISSION 181 SENT BY:PE4RS0N EDUCATION : 7- 6- 4 ; ii-4MU : PH PERMiSSiC)!S3-18&55258092092 LECAL'PeRft^l,sSi^>^s 0^r•. IJVKF, STRRBT PF ARSON UITI':H SAODU; RIVER, NJ (PAii FAX;201-2.1fi.3:!)0 livituiiiion PilONI; 201-236. July 7,2004 Rosita Moore PO Box 873 RoUa, MO 65402 F: (573) 308-1040 You have our permission to include conlcmi from our text. Family Resource Management: Principles and Applications, 2f"'. Ed. by Ruth E. Deacon & Francilte M. Firebaugh, in your disisertation or masters thesis for your course in FamJiy and Consumer Sciences Educalio7i a( Texas Tech University, Lubbock TX. Content to be included is: Page 22 Pli;asc credit our material as follows: Author, Tide, Copyright Year, and add; "Reprinted hy permission of Pearson Education, Inc., Upper Saddle River, NJ. Siiiccrcly, Permissions Adtninisu-aior 182 flUS-02-2004 16:10 HUEC 2255782697 P.02/02 COOPERATIVE EXTENSION SERVICE Knoj^ Hall, USU Campu) Ba»n Rouge Loultani 70d03 pQstOfficQ»oy»IOO Baton Rouge, Louislaru 70694-5 tOC scienter (225)S78-4J4l;FaK; (225)578-2475 |Res<'fPch % Exte ns ion] W^b fttK www.teuageBfiwr.cofT> Raswch and Kxtcraon Programs Ajrioitura Economic/Communitjr Davelopment En'IronmcHb'Nacural fVasourcas Fa/niiiea/N utn'oDn/Hailch 4-H Yoinh Program: August 2. 2004 Rosita Moore P.O. Box 873 Rolla,MO 65402 Dear Ms, Moore: You have my permission to reprint Figure 2, Page 16. from my dissertation, An Examination of the Baby Boom Generation's Financial Preparations for Retirement, for use in your doctoral dissertation. Sincerely, '^p:fc^ ' Jeanette A. Tucker, Ph.D. Associate Professor and Family Economics Specialist Louisiana Cooperative Extension Service A SutQ Parmer in ch« CooperacJve extension System The L5U AgrlciJeut^ Cernerlsa lacewlde campus of Che LSU Syjigrc, and piss.-ides equal oppertunitiw 'n programa ond cmploymfint. Lqwiiiana SaKUniveniwandA-fcM.CollflTti ,„.--,.„_^.k ...... r TOTAL p.02 183 From : todd panichlail Dear Rosita, Sorty for the delay in responding to your mail several days ago. I was on vacation, and won't be able to get back to you until later next week when I get home. With regard to your question, I will be glad to grant you a permission to reproduce any parts of my dissertation for your usage. And I'm glad that after all these years this dissertation has become useful again. Hope this helps. Regards, Tapin 184 COLLEGE STUDENT CREDIT CARD SURVEY VISA icmih hDll DDDD DDDD DDDD CONDUCTED BY: Sue Couch, Ed.D Rosita Moore, M.A. Ginny Felstehausen, Ph.D. Professor Doctoral Candidate Professor Family & Consumer Sciences Ed Family & Consumer Sciences Ed. Family & Consumer Sciences Ed. College of Human Sciences College of Human Sciences College of Human Sciences Texas Tech University Texas Tech University Texas Tech University Phone: 742-3068 Phone: 470-1228 Phone: 742-3068 DEAR SURVEY PARTICIPANT: Thank you for taking the time to complete this important questionnaire on the hnancial practices of college students. All responses will remain strictly confidential. Your name will not be associated with either the response or the results. Your participation in this study is completely voluntary, and you are free to refuse or discontinue participation at any time. Your decision to participate will not affect your present or future relationship with Texas Tech University. By completing this survey, however, you will provide the researcher with valuable information on how credit card usage is affecting today's college students. This research has been approved by the Institutional Review Board for Research involving Human Subjects at Texas Tech University. By completing this survey, you acknowledge your voluntary agreement to participate in this study. The questionnaire will take you approximately 15 to 20 minutes to complete. We appreciate your taking the time to fill out our survey If you would like your name to be entered into a drawing for $ 100 in cash, please fill out the slip below and place it in the envelope/box provided. If you have any questions about this survey, please feel free to contact the principal investigators. INSTRUCTIONS Read the directions for each question carefully. Answers should be based on your opinions, attitudes, and experiences. You may use either a pen or pencil. If you need to change an answer, please make sure your first answer is either completely erased or clearly crossed out. tear here • Name: E-mail Address: Telephone #: 1. Do you have at least one credit (NOT debit) card in your own name (e.g., VISA, Mastei<:ard, Discover, retail store, gas)? 1. Yes 2. No (If you answered "No," please skip to Question 15 and answer all that follow) 2. Please record the following information about your credit cards: Column A—the number of cards you have Column B—how often you use the cards on a monthly basis Column C—approximately how much you charge on average each month Column D—how much you owe on your cards. Column E—the highest APR (annual percentage rate) on the cards you have 1 p A B c^^'P" E •• # of cards Monthly Use Average amt Current Highest APR charged per month balance owed (annual % rate) 0-3 4-6 7-8 10+ 1. VISA, MasterCard, Discover, American Express, etc. 2. Store cards (Sears, ICPenney etc.) 3. Gas cards (Texaco, etc.) '""''^"IHHHi mm^ """'""'•""1IH 3. When did you obtain the first credit card in your own name? 1. Before entering college 2. Dunng first year of college 3. After first year of college 4. How old were you when you obtained the first credit card in your name? 1. Less than 18 2. 18-19 3. 20-21 4. 22 - 23 5. 24-25 6. Over 25 Circle the numbers below to show how influential the following factors were in your obtaining the first credit card in your name (Note: 4 = Extremely Influential and 1 = Not at all Influential) Extremely Clearly Somewhat Not at all Influential Influential Influential Influential 1. A telemarketer contacted me. 4 3 2 2. I received information from the credit card issuer in the mail. 4 3 2 3. I saw an on-campus bulletin board advertisement. 4 3 2 4. My parent(s) suggested I should get my own card. ^ 4 3 2 5. The credit card company had a promotion on campus. - •—^ ' - ^ 2 6. I saw an advertisement on the Internet. 4 3 2 7. I saw my friends using credit cards and wanted one of my own. 4 3 2 8. I was attracted to an advertisement on the radio/television. 4 3 2 9. I saw an advertisement in a newspaper/magazine. 4 3 2 0. I wanted the gifts/incentives that came with the card. 4 3 2 1. Other (please specify) 4 3 2 Page 6. What is your total credit limit (the maximum amount you can borrow on all the credit cards that you have)? I. Under $1,000 9. $15,000-$16,999 2. $1,000-$2,999 10. $17,000 - $18,999 3. $3,000 - $4,999 11. $19,000-$20,999 4. $5,000-$6,999 12. $21,000-$22,999 5. $7,000 - $8,999 13. $23,000 - $24,999 6. $9,000-$10,999 14. More than $25,000 7 $11,000-$12,999 15. I'm not sure 8. $13,000-$14,999 7. Circle the numbers below to indicate how often you used your credit cards during the past 12 months (Note: 4 = Often and 1 = Never) Often Sometimes Hardly Ever Never I. Utility/phone bills 4 3 2 : 1 2. Groceries/Household items 4 3 2 1 3. Tuition/Fees ^MMPj||||m|| 4. Insurance 4 3 2 I 5. Rent/Mortgage ,4 _,_.__ 3 ,^,^ 2 , ., J„, ,^, 6. Textbooks/school supplies 4 3 2 1 7. Entertainment/Sports 4 3 2 I 8. Eating out 4 3 2 I 9. Cash Advances 4 3 2 I 10. Emergencies (e.g. car breakdown) 4 3 2 1 11. Vacation/travel expenses 4 3 2 I W 12 Clothing 4 3 2 I 8. How many times within the past 12 months have you received a cash advance on a credit card? I None {if none, skip to Question 10) 2. 1-2 3. 3-4 4. 5-6 5. 7-10 6. More than 10 9. For what was/were the cash advance(s ) used? Circle all that apply I. Payment for another credit card 2. Utility/phone bills 3. Education-related expenses 4. Groceries/Household items mmgamm 5. Rent/Mortgage 6. msurance 7. Help me make ends meet 8. Emergency 9. Entertainment 10. Other (please specify) Page 2 10. Who pays the major part of your credit card bill(s)? Circle only one response. 1. Self 2. Spouse / Partner 3. Parent(s)/Guardians 11. Select the statement (only one) below that best describes how you generally handle monthly credit card payments. 1. Pay the minimum amount due every month on each statement 2. Pay more than the minimum amount due but not the entire balance 3. Pay each bill in full every month 4. Pay some credit card bills in full and pay only a part of the balance on others 5. luggle payments by paying some credit cards one month and others the next 6. Unable to pay even the minimum due on some cards 12. How many times during the past six months have you been late in paying any of the credit card bills for which you are responsible? 1. None 2. 1-2 3. More than 2 13. Circle the numbers below to show the importance of each statement as a reason for your using credit cards (Note: 4 = Very Important and 1 = Not at all Important.) Very Somewhat Not at all Importan)rfa t Important Important Important 1. To get cash advances 4 3 2 2. To buy a product/service immediately and pay it off later 4 3 2 3. For convenience 4 3 2 4. For safety (I don't have to carry a lot of cash) 4 3 2 1^ To cover emergency needs (e.g., unanticipated car repairs) 6. To make returning merchandise easier 4 3 2 sgjET! 7. To establish a credit record 8. For the incentives (e.g., frequent flyer miles) 4 3 2 9. To borrow for my college education 4 3 2 10. To help me make ends meet because I often run out of money 4 3 2 11. To keep up with my friends 4 3 2 Page 3 14. Circle the numbers below to indicate the extent to which you agree or disagree with each statement about the way you use and view credit cards (Note: 4 = Strongly Agree and 1 = Strongly Disagree) Strongly Strongly Agree Agree Disagree Disagree Whenever 1 use a credit card I think about what I owe. 4 3 2 Credit cards can get me into financial trouble. 4 3 2 ' -^-./i^ ^ - I don't need to think about paying off credit card bill(s) until I am out of college. 4 - . 3 2 4. With credit cards I don't have to wait to buy the things I want. 4 3 2 5. Having credit card bills can be discouraging/depressing. 4 3 2 6. When stressed out or feeling depressed, I have a greater tendency to go out and buy things using my credit card(s ). 4 3 2 7. Credit cards make it easy to buy things that I do not need1. 4 3 2 1 •*M• 8. I view my credit card purchases as spending the income I will make when I graduate. 4 3 2 9. Whenever I use a credit card I worry about paying it off. 4 3 2 1 XHH 10. It's okay to make just the minimum payment on my credit card every month. 4 3 2 11. Using credit cards tempts me to purchase more items. 4 3 2 'flHH SKIP TO QUESTION #16 ON THE FOLLOWING PAGE 15. Why do you NOT have a credit card in your name? Circle all the numbers that apply. 1. I have not applied for one, and I do not intend to. 2. I have not applied for one but intend to do so at a later date. 3. I've just applied for one but have not received it. 4. My application has been rejected. 5. I used to have at least one credit card but I have destroyed/cancelled it. 6. I use someone else's card, and I do not need my own. 7. Other (please specify) Page 4 16.Circle the numbers below to indicate the extent to which you agree or disagree with each statement (Note: 4 = Strongly Agree and 1= Strongly Disagree) Strongly Strongly Agree Agree Disagree Disagree I am not tempted by discounts or other incentives to acquire a credit card. 4 3 2 I want to acquire more credit cards than I have now. 4 3 2 I am happy to see credit card issuers on campus. 4 3 2 I dislike using credit cards. 4 3 2 It is unwise to use credit cards. 4 3 2 6. Heavy use of credit cards results in heavy debt. 4 3 2 • 7. Using a credit card will be helpful for builciini^^,^^^^^^^^ a credit history. ^IMHiiiiliHHI 4 3 2 8. The cost of using credit cards is too high. 4 3 2 9. Credit card companies should not be allowed to market their products on campus. 10. Colleges should have credit counseling available for all students. 11. I would not personaiiy cohsult a credit counselor „j •> ^' H%si;:'yyj' 'tr^^M^ on campus. „,^ >;feL-s:s=^s^g&2^.3iajsk'^u3'..... ^ 12. Taking a course in personal financial management would help me manage my hnances better 13. All freshmen should be required to take a course in personal financial management. Page 5 17. Please answer the following true or false questions by circling the appropriate number True False 1. Information on your previous credit card payment history is available to your creditors and potential creditors, your employer and potential employers, insurance companies, government agencies, and a potential landlord. 2. If your credit card has a 12% "APR", you will pay interest of 1% per month on the outstanding balance. 3. Many credit cards charge a fee for cash advances and balance transfers from other cards. 4. A poor credit history might prevent you from obtaining a car loan, a job, or an apartment. 5. It will take 30 months to pay off a $3,000 credit card balance if you pay $100 per month and you don't charge any more purchases on the card during that time. 2 6. If your credit card has a grace period of 20 days, you will not incur any interest charges on purchases made with the card if you pay off the entire balance each month within 20 days after the date on the credit card bill. !»••* 7. If your credit card has a grace period of 25 days, you will be charged interest only on purchases outstanding longer than 25 days. It is against the law for the credit card company to charge different rates for purchases, cash advances and balance transfers. 9. Credit card companies are permitted to charge you an annual fee even if you IK don't ever use the credit card. 10. If your credit history contains errors, you don't have to pay your credit card bill until the credit agency fixes the errors to your satisfaction. mp^ 11. If the interest rate on your credit card is the prime rate plus 5% and the pnme rate is currently 5%, the interest rate on your card can never go higher than 10%. 12. If you are late making a payment on your credit card, a record of this late payment will remain on your credit history for at least seven years. 13. If you have trouble paying your bills and are consistently late with your monthly credit card payment, the credit card company could raise the interest rate it is charging you. t^^mm^ sBiftg'iassag!; HKii 14. With many credit cards interest begins accruing immediately on cash advances, even if you pay off the entire balance at the end of the month (in other words, there is no grace period for cash advances). Use the following information for the last 3 statements. Tom and Fred each charge $100 every month in purchases during the nine-month school year on their credit cards ($900 in total). Both cards have the same interest rate and credit terms. Tom pays off the entire balance each month, while Fred makes the minimum payment o/ $ 10 each month and then pays off the entire remaining balance at the end of the year Both make all their payments within the stated grace period. ^Atmm^ 15a. Fred will pay more in interest charges by the end of the year 1 2 15b. Tom and Fred will each pay a total of $900, including interest. 1 2 15c. Tom will not pay any interest charges. I 2 Page 6 18. Have you ever taken a course that covered topics in personal finance/money management? 1. Yes 2. No(if No, 00 to #19) 18a. Where did you take the course? 1. In high school 2. In college 3. Other (please specify) 18b. Check the courses you have taken in which personal finance/money management principles were taught. 1. Business 2. Economics 3. Family and Consumer Sciences/Home Economics 4. Social Studies 5. Other (please specify) 19. What has been your primary source of personal financial/money management information? Check only ONE. 1. Financial services company 2. Financial literature (i.e. magazines, trade publications) 3. Friends 4. Parents 5. Course in high school 6. Course in college 7. Internet 8. Other (please specify) 20. How would you describe your understanding of personal finance? Circle the appropriate number. Not Moderately Very Knowledgeable Knowledgeable Knowledgeable Knowledgeable 12 3 4 21. With what frequency did your parents or guardians use credit cards while you were growing up? Circle the appropriate number. Never Occasionally Often Very Often Not Sure 12 3 4 5 22. Did your parents or guardians ever experience credit-related problems? Circle the appropriate number. Never Occasionally Often Very Often Do Not Know 12 3 4 5 Page 7 INFORMATION ABOUT YOUR PARENTS/GUARDIANS/STEPPARENTS {the persons with whom you lived while you were growing up) 23 Please circle the appropriate numbers to indicate the highest level of education reached by your parents/guardians/step-parents. Father Mother Did not complete high schoplj 1 Completed high school 2 2 Some college education or received Associate's degree 3 Received a Bachelor's degree 4 4 Received a Master's degree 5 5 6. Received a professional degree (CPA, ID, etc. 6 6 7. Received a Ph.D. or Ed.D. 7 7 8. I do not know. 8 24. What is the approximate annual household income of your parents/guardians/stepparents? 1. Less than $25,000 2. $25,000 - $49,999 3. $50,000 - $74,999 4. $75,000 - $99,999 5. $100,000 or more 6. I do not know INFORMATION ABOUT YOU 25.Age: 26. Gender: 1. Female 2. Male 27. Marital status: 1. Never married 2. Not married, but living with 3. Married 4. Separated 5. Divorced 6. Widowed 28.Are you a U.S. citizen? 1. Yes 2. No Pages 29. Ethnic background: 1. White/Caucasian 2. Black/African American 3. Spanish/Hispanic/Latino 4. American Indian or Alaska Native 5. Asian 6. Native Hawaiian or Other Pacific Islander 7. Multiracial/Some other race (please specify) 30. College classification: 1. Entering Freshman 2003 2. Freshman 3. Sophomore 4. lunior 5. Senior 6. Graduate student 7. Other (specify) 31. What is your major? 32. What is your college grade point average? (Note: If you are an entering freshman, write N/A.) 33. How many hours are you employed per week on- or off-campus? I. 0 2. 10 or less 3. 11-20 4. 21-30 5. 31 -40 6. More than 40 34. What is the approximate level of your outstanding student loan debt? 1. $0 (I have no student loans) 9. $13,000 - $14,999 2. Less than $1,000 10. $15,000 - $16,999 3. $1,000-$2,999 11. $17,000-$18,999 4. $3,000 - $4,999 12. $19,000 - $20,999 5. $5,000 - $6,999 13. $21,000 - $22,999 6. $7,000 - $8,999 14. $23,000 - $24,999 7. $9,000 - $10,999 15. More than $25,000 8. $11,000-$12,999 16. I'm not sure Page 9 35.What is the total amount of your consumer debt (car loans, personal loans, credit card debt, etc., but NOT mortgage)? 1. $0 (I have no consumer debt) 9. $13,000 - $14,999 2. Less than $1,000 10. $15,000 - $16,999 3. $1,000-$2,999 II. $17,000-$18,999 4. $3,000 - $4,999 12. $19,000 - $20,999 5. $5,000 - $6,999 13. $21,000 - $22,999 6. $7,000 - $8,999 14. $23,000 - $24,999 7. $9,000-$10,999 15. More than $25,000 8. $11,000-$12,999 16. I'm not sure 36. From what source(s) did you get your financial support during the past 12 months? Circle all that apply. 1. lob 2. Savings 3. Spouse/Partner 4. Gifts from family and friends 5. Parents/Guardians 6. Interest from savings or investments 7. Scholarships/Grants 8. Inheritance 9. Financial aid/Student loans 10. Credit cards/Cash advances 11. Other (please specify) 37. What was your total income before taxes during the past 12 months? Include spousal/parental support, gifts, interest from investments, scholarships, etc., but do not include money from loans, cash advances, credit cards, or anything that you have to pay back. I. Less than $3,000 7. $18,000-$20,999 2. $3,000 - $5,999 8. $21,000-$23,999 3. $6,000 - $8,999 9. $24,000 - $26,999 4. $9,000-$11,999 10. $27,000 - $29,999 5. $12,000-$14,999 11. $30,000 or more 6. $15,000-$17,999 38. Have you heard or read about the services provided by the Red to Black (R2B) program at Texas Tech? 1. Yes 2. No 39. Have you used the services of the Red to Black program? 1. Yes 2. No Thank you for participating in this research! Page 10 ^^ ^^ ^^