Measurement specificity with modern methods: Using dimensions, facets, and items from personality assessments to predict performance.
Bibliographic record
Abstract
The use of personality measures to predict work-related outcomes has been of great interest over the past several decades. The present study used machine learning (ML) to examine the optimal level in the personality hierarchy to use in developing predictive algorithms. This issue was examined in a sample of incumbent police officers (N = 1,043) who completed a multifaceted personality measure and were rated on their job performance. Criterion-related validity was investigated as a function of level of operationalization in the personality hierarchy (dimensions, facets, items), scoring method (unit weighting, ordinary least-squares regression, elastic net regression), content relevance (all items vs. job-related items), and sample size (100, 200, 300, 500, 800). Results showed that empirically derived scores outperformed unit weighting across all levels of the personality hierarchy. The highest validity estimates were consistently obtained using elastic net scoring (with hyperparameter tuning resulting in solutions closer to ridge regression) at the item level, with minimal differences between ordinary least squares and elastic net for dimensions or facets with at least moderate sample sizes (N ≥ 200). An exploratory modeling approach where all item content was used did not outperform scoring when the item pool was relegated to only job-relevant personality traits. Taken together, findings suggest that personality scoring should occur at narrow operationalizations down to at least the facet level. In addition, this study demonstrated how ML can be used to not only maximize criterion-related validity but also to test long-standing theoretical problems in the organizational sciences. (PsycInfo Database Record (c) 2022 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 | 0.057 | 0.150 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".