Surveying for “artifacts”: The susceptibility of the OCB–performance evaluation relationship to common rater, item, and measurement context effects.
Bibliographic record
Abstract
Despite the increased attention paid to biases attributable to common method variance (CMV) over the past 50 years, researchers have only recently begun to systematically examine the effect of specific sources of CMV in previously published empirical studies. Our study contributes to this research by examining the extent to which common rater, item, and measurement context characteristics bias the relationships between organizational citizenship behaviors and performance evaluations using a mixed-effects analytic technique. Results from 173 correlations reported in 81 empirical studies (N = 31,146) indicate that even after controlling for study-level factors, common rater and anchor point number similarity substantially biased the focal correlations. Indeed, these sources of CMV (a) led to estimates that were between 60% and 96% larger when comparing measures obtained from a common rater, versus different raters; (b) led to 39% larger estimates when a common source rated the scales using the same number, versus a different number, of anchor points; and (c) when taken together with other study-level predictors, accounted for over half of the between-study variance in the focal correlations. We discuss the implications for researchers and practitioners and provide recommendations for future research.
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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 | 0.273 | 0.572 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".