Modeling Response Styles in Cross‐Country Self‐Reports: An Application of a Multilevel Multidimensional Nominal Response Model
Why this work is in the frame
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Bibliographic record
Abstract
Abstract We examined the feasibility and results of a multilevel multidimensional nominal response model (ML‐MNRM) for measuring both substantive constructs and extreme response style (ERS) across countries. The ML‐MNRM considers within‐country clustering while allowing overall item slopes to vary across items and examination of whether certain items were more prone to ERS. We applied this model to survey items from TALIS 2013. Results indicated that self‐efficacy items were more likely to trigger ERS compared to need for professional development, and the between‐country relationships among constructs can change due to ERS. Simulations assessed the estimation approach and found adequate recovery of model parameters and factor scores. We stress the importance of additional validity studies to improve the cross‐cultural comparability of substantive constructs.
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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 | 0.056 | 0.076 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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