Measurement specificity with modern methods: Using dimensions, facets, and items from personality assessments to predict performance.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it