Socially desirable responding and its elusive effects on the validity of personality assessments.
Bibliographic record
Abstract
Past studies of socially desirable self-reports on the items of personality measures have found inconsistent effects of the response bias on the measures' predictive validities, with some studies reporting small effects and other studies reporting large effects. Using Monte Carlo methods, we evaluated various models of socially desirable responding by systematically adding predetermined amounts of the bias to the simulated personality trait scores of hypothetical test respondents before computing test-criterion validity correlations. Our study generally supported previous findings that have reported relatively minor decrements in criterion prediction, even with personality scores that were massively infused with desirability bias. Furthermore, the response bias failed to reveal itself as a statistical moderator of test validity or as a suppressor of validity. Large differences between some respondents' obtained test scores and their true trait scores, however, meant that the personality measure's construct validity would be severely compromised and, more specifically, that estimates of those individuals' criterion performance would be grossly in error. Our discussion focuses on reasons for the discrepant results reported in the literature pertaining to the effect of socially desirable responding on criterion validity. More important, we explain why the lack of effects of desirability bias on the usual indicators of validity, moderation, and suppression should not be surprising.
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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 | 0.175 | 0.507 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".