Relationships between faking, validity, and decision criteria in personnel selection.
Bibliographic record
Abstract
There has been some debate in recent years as to whether faking on personality tests, while apparently not affecting criterion-related validity, still has a detrimental effect on the accuracy of hiring decisions. The present paper is set out to contribute to a clarification of this issue conceptually and empirically. In the conceptual part, statistical parameters of test scores obtained in selection settings that may affect validity and hiring decisions are disentangled. A data set of job incumbents who took an integrity test in a research setting is then used to demonstrate the effects of simulated faking scores with systematically manipulated distributional properties. Results show that, while hiring decisions are more sensitive to manipulations than validity, changes on both decisions and validity depend upon the same parameters, most importantly on variance in faking. Unlike the overlap between decisions based on faked and non-faked scores, the accuracy of these decisions was not more sensitive to faking than validity, regardless of selection ratio. Results are discussed in light of findings on criterion-related validity of personality tests in real-world applicant settings.
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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 | 0.051 | 0.323 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".