Bridging Occupational Preferences: How RIASEC's People-Things and Data-Ideas Dimensions Relate to Relativized Occupational Personality Traits
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Introduction: The traditional RIASEC model organizes occupational preferences into a circular structure with two orthogonal dimensions: People-Things and Data-Ideas. In contrast, a more recent model of occupational personality traits (O-E-A) emphasizes Openness to Experience (Openness), Extraversion, and Agreeableness within a three-cluster circular arrangement, which does not follow the R-I-A-S-E-C order. Do these frameworks represent distinct constructs or share an underlying structure?Methods: This study investigated whether applying RIASEC's relative, within-person scoring procedure to the O-E-A framework, originally based on absolute scoring, could reveal shared orthogonal dimensions: People-Things (A-S-E to C-R-I) and Data-Ideas (E-C to I-A). The analysis involved reanalyzing large-scale data in which participants rated themselves on the Big Five personality traits and on their preferences for 13 O-E-A job categories. Multiple linear regressions (MLR) and canonical correlation analyses (CCA) were conducted to explore the relationships between these variables, with job preferences transformed into relative values.Results: Extraversion and Openness emerged as the most influential predictors of 13 relative preferences in the MLR analysis. The CCA revealed two orthogonal preference dimensions corresponding to People-Things (Extraversion-Introversion) and Data-Ideas (Openness-Closedness). The RIASEC sequence emerged within the relativized O-E-A framework, revealing two orthogonal axes: Extraversion (C-R-I to A-S-E) and Openness (E-C to I-A).Discussion: These findings suggest that discrepancies between the O-E-A personality model and RIASEC preferences may partly arise from differences in scoring methods. Relative scoring highlights how individuals prioritize one job domain over another, revealing a shared motivational structure underlying both frameworks. These results support integrating personality-based occupational insights with traditional preference models.
