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Previous demographic research indicates that the computer revolution has segregated the sexes--males use computers for programming and females use computers for application programs. Attempts to explain this bias in terms of cognitive skills have faced criticism for promoting differences that are statistically significant, but not theoretically meaningful. The goal of the present study was to determine the extent to which attitudes towards computers (i.e., anxiety, confidence, liking, and usefulness) and sex role variables (masculine and feminine) might explain this bias among high school students. Fishbein and Ajzen's Theory of Reasoned Action was the theoretical model used to explain how these variables interact to determine computer use.
Hypotheses were tested to determine (1) the extent adolescent computer users can be differentiated by these variables, (2) how the variables differ within and between the groups of users, and (3) the extent to which the sexes follow traditional sex role orientations. The subjects for this study were 462 high school students from six public high schools in Philadelphia. They were divided into six groups based on a cross tabulation of their intensity of recent computer use (programmers, application users, and general users) and biological sex. Attitudes were assessed using Loyd's Computer Attitude Scale and sex role self-concept was assessed using the Bem Sex-Role Inventory. Data were collected during Fall 1989.
The data indicate that attitude and sex role variables can be used to differentiate among types of computer users. The data also challenges the findings of previous research concerning sex differences. While male programmers tended to have more positive attitudes than female programmers, there was a lack of sex differences in attitudes within the remaining groups. In addition, the data indicate that female programmers tend to balance both masculine and feminine attributes, while the remaining user groups maintain sex role-biological sex congruence.
The implications of these results for the identification of students who might succeed in computer courses is discussed. Future research needs to focus on using the variables to predict achievement and on the impact of the peer group on the user.