Two psychological factors were included in the analyses: Money ethic and locus of control. A money ethic scale was adapted from Tang (1995). The scale included 12 items. Each item was measured with a 4-point Likert-type scale, ranging from strongly agree (4) to strongly disagree (1). Examples of money ethic items include: (a) money is important, (b) money is evil, (c) money makes people respect me in the community, and (d) money can give me the opportunity to be what I want to be. Some items were reverse coded to represent a homogeneous direction. Higher scores were interpreted to mean that the respondent valued money and its attributes to a greater extent than 本文来自^辣%文~论*文&网原文请找腾讯3249'114 coefficient of the index was .7461.
A total of 14 questions measuring locus of control were adopted from Levenson (1973). Locus of control items were also measured with a 4-point Likert-type scale, with 1 representing strongly disagree and 4 indicating strongly agree. Examples of locus of control scale items include: (a) I am usually able to protect my personal interests; (b) when I get what I want, it's usually because I'm lucky; (c) to a great extent my life is controlled by accidental happenings; and (d) when I make plans, I am almost certain to make them work. Some items were reverse coded. Higher scores represented an external locus of control, suggesting that these individuals felt that they had less control over their lives than others. Possible scores ranged from 14 to 56, with a mean score of 41.87. The reliability of the scale was .7040. Table 2 summarizes the measurement of the variables in this study. (see table 2)
Analysis
Descriptive analyses were performed to examine the demographic characteristics and credit card use behaviors of the sample respondents. A least squares multiple regression was conducted to examine the factors associated with credit attitudes. A correlation matrix was developed and examined to diagnose the possibility of multicollinearity between and among the variables. Potential multicollinearity issues were further examined with SPSS collinearity diagnosis techniques using tolerance, VIF, and eigenvalues as suggested by Hair, Anderson, Tatham, and Black (1995). Because of the high correlation between age and marital status, and the resulting possible collinearity problem, marital status was excluded in the final regression analysis.
Results
Respondents' ages ranged from 18 to 47, with a mean age of 21 years old. More than one half of the respondents were female (57%), and the majority (86.8%) were never married. About one-fifth (21.5%) of the respondents lived on campus. The remaining (64.5%) students lived in rental housing. The majority of the students were White or Caucasian (86.8%), while 6.6% of the respondents were either Hispanic or Latino. The majority of the students (71.5%) were human sciences majors; 14.9% were business majors, and 9.1% were arts and sciences majors. In terms of academic level, 15.3% of respondents were freshmen, 28.1% were sophomores, 26.4% were juniors, 26.4% were seniors, with the remainder (3.8%) being graduate students. More than one-half (56.4%) of the students were employed part-time, and 34.9% were not employed. Twenty-one students indicated that they were employed full-time.
Table 3 shows the credit card use behaviors of the survey respondents. The majority of students (70.7%) held one or more credit cards. This figure matches the national average. More than 10% of the students possessed five or more credit cards. On average, college students received their first credit card when they were 18 years old. The survey indicated that some students obtained their first credit card as young as 15 years of age. It was also found that about one quarter (22%) of the students never keep copies of their charge slips, and they seldom check receipts against their monthly credit statement. On the other hand, almost half (49.4%) of the students paid their credit bills in full. While this seems positive, almost 10% of the students paid only the minimum payment in any given month.
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