Title

A predictive psychometric model to identify personality and gender differences of college majors.

SelectedWorks Author Profiles:

Michael G. Luckett

Philip J. Trocchia

Document Type

Article

Publication Date

2016

Date Issued

January 2016

Date Available

June 2016

ISSN

1472-8117

Abstract

This research applies Cattell's 16 Personality Factor Questionnaire (16PF) (Cattell & Schuerger, 2003) to compare and contrast personality traits among undergraduate men and women enrolled in business and liberal arts colleges. Specific attention is given to what personality differences exist between accounting as the most popular business major, and that of psychology as the most popular liberal arts major. For added comparison, we further juxtaposed marketing, which contains a combination of consumer psychology and analytical business skills. Analysis of variance among the three majors found the differences in 10 personality factors to be significant and a multivariate analysis of variance determined gender a significant covariate. While this research provides a detailed personality profile unique for each major, stepwise discriminant analysis isolated one personality factor providing a predictive model of 42.8% while gender contributed 5.1% for a predictive psychometric model of 47.9%. This research is unique as it compares personality differences among business majors with a popular nonbusiness major, isolates the impact of gender, and provides a parsimonious predictive model that can be used to identify a compatible fit between personality and gender by college major.

Comments

Abstract only. Full-text article is available through licensed access provided by the publisher. Published in The International Journal of Management Education, 14, 240-247. doi: 10.1016/j.ijme.2016.05.004. Members of the USF System may access the full-text of the article through the authenticated link provided.

Language

en_US

Publisher

Elsevier

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.