A Bayesian hierarchical modeling approach of person and item features that contribute to response bias
Bibliographic record
Abstract
Traditionally, extreme responding has been recognized as a bias in which individuals tend to select the highest or lowest response categories on self-report items, regardless of item content. This bias is believed to occur independently of the desirability of the trait or state described in the item statement. However, little effort has been made to empirically test this assumption. In this study, we propose a novel approach to control for both extreme (ERS) and socially desirable responding (SDR) in self-report questionnaires. We considered judges' ratings of item social desirability to control for SDR by employing Bayesian hierarchical and joint modeling. We also considered the effect of ERS to be multiplicative rather than additive, assuming a noncompensatory role to ERS. The advantage of this approach is mostly computational. Overall, our results indicate that modeling the impact of ER improved the model fit on measures of affect and pathological traits. However, contrary to expectations, our analysis did not indicate any substantive influence of social desirability on items’ difficulty. In conclusion, our findings favor the perspective of both ER and social desirability as true traits rather than merely nuisance factors, evidence that our approach may assist researchers in gaining a deeper understanding of response biases in self-report items.
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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 | 0.050 | 0.099 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".