COMPUTER ANXIETY AND COMPUTER SELF-EFFICACY

Inhaltsverzeichnis:

  1. Title
  2. Abstract
  3. Introduction
  4. Method
  5. Results
  6. Discussion
  7. References
  8. Appendix A
  9. Appendix B
  10. Table 1
  11. Table 2
  12. Footnotes
  13. Contributor

Results

The item sets for CAX and CSE were analysed test- theoretically on the basis of the pretest data and with respect to the Partial Credit Model (PCM) of Masters (1982). The PCM was developed by generalizing the Rasch Model to items with more than two response categories. In contrast to the model of Classical Test Theory, which is usually applied in this context, the PCM has three important advantages: 1) it explicitly refers to a limited range of response categories for each item; 2) it allows empirical testing of whether the reponse categories are actually ordered ordinally with respect to the variable to be measured; 3) if it fits to data then it provides a solid empirical and theoretical basis for measurements - at least - on interval scale level (cf. Rost, 1996). All test-theoretical analyses were performed with WINMIRA (see Davier, 1997).
The two item sets for CAX and CSE were analysed separately. Items with disordinal response categories or with a bad fit to the PCM were eliminated stepwise. For CAX 13 items had to be eliminated. The remaining 7 items are 'Thinking about taking a computer course', 'Applying for a job requiring computer training', 'Sitting in front of a home computer', 'Learning to write programs', 'Learning computer terminology', 'Reading a computer manual', and 'Taking a class in the use of computers'. The reliability for these seven items is 0.84. To analyse the CSE-items, the two easier response categories had to be comprised into one, because for one item nobody chose the easiest category. Thus, the PCM reduces to the ordinary Rasch Model. All nine items correspond extremely well to this model. No item needed to be eliminated. The reliability for the nine items is 0.83. On the basis of the remaining items, pretest scale values for CAX and CSE were determined by weighted maximum likelihood estimation (Warm, 1989). The correlation between both scales is -0.50 (p<0.001; two-tailed) and -0.60 after correction for attenuation; i.e. CAX decreases strongly with CSE. Moreover, nearly all further questionnaire variables indicating previous computer use or computer performance correlate negatively with CAX and positively with CSE.
Subjects participating in both tests have no significantly different pretest-values in CAX or CSE than subjects participating only in the first test. Nor is there any significant interaction between posttest participation, computer lecture and/or computer course attendance. This indicates that neither CAX nor CSE influenced course dropout. For the 41 pretest-posttest subjects, neither computer lecture attendance, nor computer course attendance, nor the interaction between both produced a significant effect on pretest values of CAX or CSE. This indicates that the four groups in the factorial design are at least roughly comparable. With scale values normed to zero as the lower and one hundred as the upper scale limit, the total pretest means of the pretest-posttest subjects are 38.01 (sd=14.85) for CAX and 48.49 (sd=16.15) for CSE. As both values lie in the middle of the scale, possible changes can be detected.
The posttest values of the 41 pretest-posttest subjects were determined by assigning measurement values to raw scores according to the assignment function that was calculated according to the PCM for the 249 pretest subjects. Recourse to pretest data was necessary because the posttest data were too few to allow a reasonable test- theoretical analysis in its own right. The changes in CAX and CSE were determined by subtracting pretest from posttest values (see table 2). CAX reduced both for students who attended the computer course and for those who did not; however, it reduced significantly more for students who did (F(1,37)=6.185, p<0.05). CSE enlarged for students who attended the course and reduced slightly for students who did not. The difference between the changes is significant (F(1,37)=4.915, p<0.05). Neither lecture attendance nor the interaction between course and lecture attendance produced a significant effect; - neither for CAX, nor for CSE. There was even a non-significant increase of CAX for subjects who attended the lecture but did not attend the course.

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