A Meta‐Analytic Multitrait Multirater Separation of Substance and Style in Social Desirability Scales
Why this work is in the frame
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Bibliographic record
Abstract
Though unlikely virtues scales have a long history in personality, clinical, and applied psychology for detecting socially desirable responding, using such social desirability (SD) scales has generally failed to improve the validity of personality measures. We examined whether this is because (a) response distortion itself has minimal impact on personality's validity, (b) SD scales are ineffective at assessing response distortion, or (c) SD scales are conflated with substantive trait variance. We compiled a meta-analytic multitrait multimethod matrix consisting of multirater personality traits, SD scales, and performance outcomes. We examined the influence of trait factors and self-report method factors on SD scales and performance. We found that self-report method variance (a) was negatively related to performance, (b) would suppress personality-performance relationships for self-report measures, and (c) was (partially) assessed by SD scales. However, relative to the effects of self-report method variance, SD scales are even more strongly influenced by Conscientiousness, Emotional Stability, and Agreeableness. It is not the case that SD scales are insensitive to inflated responding but that their susceptibility to personality trait variance likely outweighs their benefits. We discuss the implications of these results for using SD scales in research and practice.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 it