Psychometric Analysis of Stöber's Social Desirability Scale (SDS—17): An Item Response Theory Perspective
Bibliographic record
Abstract
Stöber's Social Desirability Scale (SDS-17) was examined psychometrically in 5 samples (N=2817) from Austria, Canada, and the U.S.A. Rasch and Mokken scaling analyses attested the SDS-17 is not strictly unidimensional. Age, agreeableness, and conscientiousness were notable positive correlates of SDS-17 scores. There were signs of non-normal score distributions, acquiescence bias, and sex and country differences (higher scores among Austrians than North Americans). Items with higher ratings of social desirability according to previous research were particularly prone to show sex effects. The SDS-17 appears suitable in cross-cultural settings, but may benefit from substituting its true-false response format with a rating-scale format. A formative-indicators view regarding the social desirability construct and the SDS-17 is discussed.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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 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".