Departures from linearity as evidence of applicant distortion on personality tests
Bibliographic record
Abstract
Abstract Two field studies were conducted to examine how applicant faking impacts the normally linear construct relationships of personality tests using segmented regression and by partitioning samples to evaluate effects on validity across different ranges of test scores. Study 1 investigated validity decay across score ranges of applicants to a state police academy (N = 442). Personality test scores had nonlinear construct relations in the applicant sample, with scores from the top of the distribution being worse predictors of subsequent performance but more strongly related to social desirability scores; this pattern was not found for the partitioned scores of a cognitive test. Study 2 compared the relationship between personality test scores and job performance ratings of applicants (n = 97) to those of incumbents (n = 318) in a customer service job. Departures from linearity were observed in the applicant but not in the incumbent sample. Effects of applicant distortion on the validity of personality tests are especially concerning when validity decay increases toward the top of the distribution of test scores. Observing slope differences across ranges of applicant personality test scores can be an important tool in selection.
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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 | 0.023 | 0.098 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".