title: People--things and data--ideas: bipolar dimensions?
abstract: We examined a longstanding assumption in vocational psychology that people-things and data-ideas are bipolar dimensions. Two minimal criteria for bipolarity were proposed and examined across 3 studies: (a) The correlation between opposite interest types should be negative; (b) after correcting for systematic responding, the correlation should be greater than -.40. In Study 1, a meta-analysis using 26 interest inventories with a sample size of 1,008,253 participants showed that meta-analytic correlations between opposite RIASEC (realistic, investigative, artistic, social, enterprising, conventional) types ranged from -.03 to .18 (corrected meta-analytic correlations ranged from -.23 to -.06). In Study 2, structural equation models (SEMs) were fit to the Interest Finder (IF; Wall, Wise, & Baker, 1996) and the Interest Profiler (IP; Rounds, Smith, Hubert, Lewis, & Rivkin, 1999) with sample sizes of 13,939 and 1,061, respectively. The correlations of opposite RIASEC types were positive, ranging from .17 to .53. No corrected correlation met the criterion of -.40 except for investigative-enterprising (r = -.67). Nevertheless, a direct estimate of the correlation between data-ideas end poles using targeted factor rotation did not reveal bipolarity. Furthermore, bipolar SEMs fit substantially worse than a multiple-factor representation of vocational interests. In Study 3, a two-way clustering solution on IF and IP respondents and items revealed a substantial number of individuals with interests in both people and things. We discuss key theoretical, methodological, and practical implications such as the structure of vocational interests, interpretation and scoring of interest measures for career counseling, and expert RIASEC ratings of occupations.
authors.affiliation: Department of Psychology, University of Illinois at Urbana-Champaign, Champaign, IL 61820, USA. sientay@illinois.edu
authors.forename: Louis
authors.initials: L
authors.lastname: Tay
authors.forename: Rong
authors.initials: R
authors.lastname: Su
authors.forename: James
authors.initials: J
authors.lastname: Rounds
mesh_terms: D002321:Career Choice; D005163:Factor Analysis, Statistical; D005260:Female; D006801:Humans; D008297:Male; D009790:Occupations; D010551:Personality; D010555:Personality Inventory / Q000706:statistics & numerical data*; D011594:Psychometrics; D014830:Vocational Guidance / Q000379:methods*