Improving measurement in health education and health behavior research using item response modeling: introducing item response modeling
Bibliographic record
Abstract
This paper is the first of several papers designed to demonstrate how the application of item response models in the behavioral sciences can be used to enhance the conceptual and technical toolkit of researchers and developers and to understand better the psychometric properties of psychosocial measures. The papers all use baseline data from the Behavior Change Consortium data archive. This paper begins with an introduction to item response models, including both dichotomous and polytomous versions. The concepts of respondent and item location, model interpretation, standard errors and testing model fit are introduced and described. A sample analysis based on data from the self-efficacy scale is used to illustrate the concepts and techniques.
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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 | 0.209 | 0.347 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.008 | 0.015 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".