One Size Does Not Fit All: Unraveling Item Response Process Heterogeneity Using the Mixture Dominance-Unfolding Model (MixDUM)
Why this work is in the frame
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Bibliographic record
Abstract
When modeling responses to items measuring non-cognitive constructs that require introspection (e.g., personality, attitude), most studies have assumed that respondents follow the same item response process—either a dominance or an unfolding one. Nevertheless, the results are not equivocal, as some preliminary evidence suggests that some people use an unfolding response process, whereas others use a dominance response process. To enhance item response modeling, it is critical to develop measurement models that can accommodate heterogeneity in the item response processes. Therefore, we proposed the Mixture Dominance-Unfolding Model (MixDUM) to formally identify this potential population heterogeneity. Monte Carlo simulations showed that MixDUM possessed reasonably good statistical properties. Moreover, ignoring item response process heterogeneity was detrimental to item parameter estimation and led to less accurate selection outcomes. An empirical study was conducted in which respondents completed focal personality scales under either an honest condition or a simulated job application condition, to demonstrate the utility of MixDUM. The findings indicated (1) that MixDUM provided the best fit across scales, (2) that approximately 55–60% of respondents utilized an unfolding response process, (3) that respondents exhibited moderate consistency in their use of response processes across scales, (4) that narcissism consistently negatively predicted the use of an unfolding response process, and (5) that the criterion-related validity of focal personality scores varied across latent classes for certain criteria. To encourage its use, we provided a tutorial on the implementation of MixDUM in the R package mirt .
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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 | 0.015 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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