title: Predicting student enrollment and persistence in college STEM fields using an expanded P-E fit framework: a large-scale multilevel study.
abstract: Using an expanded person-environment fit (P-E fit) model, we conducted 2 studies to test the combined effects of 2 individual difference factors, ability-demand fit and interest-vocation fit, in predicting college student choice of and persistence in the science, technology, engineering, and mathematics (STEM) fields. Analysis results based on data from 207,093 students entering 51 postsecondary institutions supported the hypothesized roles that academic ability and interest fit play in determining STEM field choice and persistence. Ability was found to moderate the effects of interest fit on the behavioral outcomes, thus expanding the P-E fit framework. We also found that gender moderates the effects of these individual difference predictors, such that the effects are weaker for females than for males in predicting STEM choice. For STEM persistence, the opposite effect was found: The relationship between ability and persistence is stronger for females than it is for males. As such, this research contributes to the resurging attention in the roles that individual difference factors play in organizational and educational research and the importance of integrating ability and interest constructs to fully understand college and career choice and persistence behaviors.
authors.affiliation: Department of Management, Entrepreneurship and Technology, University of Nevada.
authors.forename: Huy
authors.initials: H
authors.lastname: Le
authors.affiliation: Research and Development, Educational Testing Service.
authors.forename: Steven B
authors.initials: SB
authors.lastname: Robbins
authors.affiliation: Statistical Research Services, ACT Inc.
authors.forename: Paul
authors.initials: P
authors.lastname: Westrick
mesh_terms: D000328:Adult; D001076:Aptitude; D002321:Career Choice; D004738:Engineering / Q000706:statistics & numerical data*; D005260:Female; D006801:Humans; D007206:Individuality; D008297:Male; D008433:Mathematics / Q000706:statistics & numerical data*; D008962:Models, Theoretical; D012586:Science / Q000706:statistics & numerical data*; D013672:Technology / Q000706:statistics & numerical data*; D014495:Universities / Q000706:statistics & numerical data; D055815:Young Adult