The Use of Random Coefficient Modeling for Understanding and Predicting Job Performance Ratings: An Application with Field Data
Bibliographic record
Abstract
Earlier research using confirmatory factor analysis (CFA) suggests that most variance in job performance ratings is not attributable to ratee main effects. In this article, the authors point out several issues associated with CFA methodology and argue that random coefficient modeling (RCM) can be a useful alternative for estimating variances associated with ratee main effects, rater main effects, and the upper bound of Rater × Ratee interaction effects. Using an application of RCM on field data, the authors found that rater main effects variance was nearly two times as large as ratee main effects variance. They report meaningful contingencies of these findings by modeling rater familiarity with the ratee and the number of ratees rated by a rater. Finally, interactions revealed that Conscientiousness-related variables were positively related to job performance only when rater familiarity with the ratee was high or the number of ratees rated was high. The authors discuss how the RCM methodology can be used to assess the construct validity of job performance ratings and to test substantive hypotheses involving variance components, main effects, and interactions within nonindependent observations.
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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 | 0.071 | 0.219 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".