Improving measurement in health education and health behavior research using item response modeling: comparison with the classical test theory approach
Bibliographic record
Abstract
This paper compares the approach and resultant outcomes of item response models (IRMs) and classical test theory (CTT). First, it reviews basic ideas of CTT, and compares them to the ideas about using IRMs introduced in an earlier paper. It then applies a comparison scheme based on the AERA/APA/NCME 'Standards for Educational and Psychological Tests' to compare the two approaches under three general headings: (i) choosing a model; (ii) evidence for reliability--incorporating reliability coefficients and measurement error--and (iii) evidence for validity--including evidence based on instrument content, response processes, internal structure, other variables and consequences. An example analysis of a self-efficacy (SE) scale for exercise is used to illustrate these comparisons. The investigation found that there were (i) aspects of the techniques and outcomes that were similar between the two approaches, (ii) aspects where the item response modeling approach contributes to instrument construction and evaluation beyond the classical approach and (iii) aspects of the analysis where the measurement models had little to do with the analysis or outcomes. There were no aspects where the classical approach contributed to instrument construction or evaluation beyond what could be done with the IRM approach. Finally, properties of the SE scale are summarized and recommendations made.
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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 | 0.263 | 0.452 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".