PERSONALITY DIMENSIONS EXPLAINING RELATIONSHIPS BETWEEN INTEGRITY TESTS AND COUNTERPRODUCTIVE BEHAVIOR: BIG FIVE, OR ONE IN ADDITION?
Bibliographic record
Abstract
Although the criterion‐related validity of integrity tests is well established, there has not been enough research examining which personality constructs contribute to their criterion‐related validity. Moreover, evidence of how well findings on integrity tests in North America generalize to non‐English speaking countries is virtually absent. This research addressed these issues with data obtained from employees and students in Canada and Germany (total N= 853). Specifically, we tested the hypotheses that (a) Honesty–Humility, as specified in the HEXACO model of personality, is relatively more important than the Big 5 dimensions of personality in accounting for the criterion‐related validity of overt integrity tests, whereas (b) the Big 5 are relatively more important in explaining the validity of personality‐based integrity tests. These predictions were tested using 2 criteria (counterproductive work behavior and counterproductive academic behavior) as well as 2 overt and 2 personality‐based integrity tests. We found evidence of the expected differences between types of integrity tests largely regardless of culture of the sample, specific test, criterion, or population under research, pointing to some degree of generalizability of findings in integrity testing research. Implications include theoretical refinements in research on integrity testing and encouragement of practical applications beyond North America.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".