Extraversion advantages at work: A quantitative review and synthesis of the meta-analytic evidence.
Bibliographic record
Abstract
How and to what extent does extraversion relate to work relevant variables across the lifespan? In the most extensive quantitative review to date, we summarize results from 97 published meta-analyses reporting relations of extraversion to 165 distinct work relevant variables, as well as relations of extraversion's lower order traits to 58 variables. We first update all effects using a common set of statistical corrections and, when possible, combine independent estimates using second-order meta-analysis (Schmidt & Oh, 2013). We then organize effects within a framework of four career domains-education, job application, on the job, and career/lifespan-and five conceptual categories: motivations, values, and interests; attitudes and well-being; interpersonal; performance; and counterproductivity. Overall, extraversion shows effects in a desirable direction for 90% of variables (grand mean ρ̄ = .14), indicative of a small, persistent advantage at work. Findings also show areas with more substantial effects (ρ̄ ≥ .20), which we synthesize into four extraversion advantages. These motivational, emotional, interpersonal, and performance advantages offer a concise account of extraversion's relations and a new lens for understanding its effects at work. Our review of the lower order trait evidence reveals diverse relations (e.g., the positive emotions facet has consistently advantageous effects, the sociability facet confers few benefits, the sensation-seeking facet is largely disadvantageous), and extends knowledge about the functioning of extraversion and its advantages. We conclude by discussing potential boundary conditions of findings, contributions and limitations of our review, and new research directions for extraversion at work. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.032 | 0.110 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.014 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".