The effectiveness of precollege and college achievement measures in predicting persistence for men and women in computer-related fields was examined. Persistence rates were similar for men and women in computer technology, electrical/computer engineering, and industrial engineering. However, fewer women than men persisted in computer science. Discriminant function analysis was conducted separately for men and women in each field and was equally effective for men and women in correctly classifying persisters and nonper-sisters (64–72%) in each field except computer technology where the classification rate was considerably lower for women (58%). GPA was generally the most important variable followed by a measure of math ability. High school science grades and number of semesters were often selected for the discriminant function for men, but not for women. The potential role of nonachievement factors in persistence such as peer and faculty support and expectancies are also discussed.
Get full access to this article
View all access options for this article.
References
1.
ColeJ. R., Women in Science, American Scientist, 69, pp. 385–391, 1981.
2.
SellsL. W., The Mathematics Filter and the Education of Women and Minorities, in Women and the Mathematical Mystique, FoxL. H.BradyL.TobinD. (eds.), The Johns Hopkins University Press, Baltimore, 1980.
3.
MeeceJ. L.Eccles-ParsonsJ.KaczalaC. M.GoffS. B.FuttermanR., Sex Differences in Math Achievement: Toward a Model of Academic Choice, Psychological Bulletin, 91, pp. 324–348, 1982.
4.
DavisS. O., A Researcher's-eye View: Women Students, Technical Majors, and Retention, IEEE Transactions on Education, E-18, pp. 25–29, 1975.
5.
KaufmanH., Women Engineering Students: Many Switch Out, Many Transfer In, New Engineer, 6, pp. 19–22, 1977.
6.
OttM. D., Retention of Men and Women Engineering Students, Research in Higher Education, 9, pp. 137–150, 1978.
7.
GardnerR. E., Women in Engineering: The Impact of Attitudinal Differences on Educational Institutions, Engineering Education, 67, pp. 233–240, 1976.
8.
JagacinskiC. M.LeBoldW. K., A Comparison of Men and Women Undergraduate and Professional Engineers, Engineering Education, 72, pp. 213–220, 1981.
9.
FosterR. J., Retention Characteristics of Engineering Freshmen, Engineering Education, 66, pp. 724–728, 1976.
10.
LeBoldW. K.ShellK. D., The Utility of Cognitive and Noncognitive Information in Predicting Engineering Retention and Selection of Specialization, in 1980 Frontiers in Education Conference Proceedings, GraysonL. P.BeidenbachJ. M. (eds.), IEEE and ASEE, New York, pp. 504–510, 1980.
11.
PantagesT. J.CreedonC. F., Studies of College Attrition: 1950–1975, Review of Educational Research, 48, pp. 49–101, 1978.
12.
CampbellP. F.McCabeG. P., Predicting the Success of Freshmen in a Computer Science Major, Communications of the ACM, 27, pp. 1108–1113, 1984.
13.
MiuraI. T.HessR. D., Sex Differences in Computer Access, Interest and Usage, paper presented at the annual convention of the American Psychological Association, Anaheim, California, August, 1983.
14.
ChenM., Gender and Computers: The Beneficial Effects of Experience on Attitudes, Journal of Educational Computing Research, 2, pp. 265–282, 1986.
15.
HessR. D.MiuraI. T., Gender Differences in Enrollment in Computer Camps and Classes, Sex Roles, 13, pp. 193–204, 1985.
16.
BenbowC. P.StanleyJ. C., Gender and the Science Major: A Study of Mathematically Precocious Youth, in Advances in Motivation and Achievement, Vol. 2, Women in Science, SteinkampM. W.MaehrM. L. (eds.), JAI Press Inc., Greenwich, Connecticut, 1984.
17.
AstinA. W., Preventing Students from Dropping Out, Jossey-Bass Publishers, San Francisco, 1977.
18.
PascarellaE. T.TerenziniP. T., Predicting Voluntary Freshman Year Persistence/Withdrawal Behavior in a Residential University: A Path Analytic Validation of Tinto's Model, Journal of Educational Psychology, 75, pp. 215–226, 1983.
19.
PascarellaE. T.ChapmanD. W., A Multiinstitutional, Path Analytic Validation of Tinto's Model of College Withdrawal, American Educational Research Journal, 20, pp. 87–102, 1983.
20.
FowlerG. C.GlorfeldL. W., Predicting Aptitude in Introductory Computing: A Classification Model, AEDS Journal, 14, pp. 96–191, 1981.
21.
PetersenC. C.HoweT. G., Predicting Academic Success in Introduction to Computers, AEDS Journal, 12, pp. 182–191, 1979.
22.
Eccles-ParsonsJ., Sex Differences in Mathematics Participation, in Advances in Motivation and Achievement, Vol. 2, Women in Science, SteinkampM. W.MaehrM. L. (eds.), JAI Press Inc., Greenwich, Connecticut, 1984.
23.
DeBoerG. E., Success and Failure in the 1st Year of College: Effects on Expectations, Affect, and Persistence, Journal of College Student Personnel, 26, pp. 234–239, 1985.
24.
BrownL. H., Involvement of Students in Research Projects, in Women in Engineering — Beyond Recruitment, Conference Proceedings, OttM. D.ReeseN. A. (eds.), Cornell University, Ithaca, New York, pp. 98–105, 1975.
25.
FoxL. H.TobinD.BrodyL., Sex-Role Socialization and Achievement in Mathematics, in Sex-Related Differences in Cognitive Functioning, WittigM. A.PetersenA. C. (eds.), Academic Press, New York, 1979.
