Measure for Measure: New Developments in Measurement and Item Response Theory
Bibliographic record
Abstract
For the past 70 years, test development has been dominated by what is called classical test theory (CTT). However, there are many problems associated with CTT, including: the resulting scales tend to be long; their interpretation is highly dependent on the normative sample; the assumption that each item contributes equally to the total score is often wrong, as is calculating a single index of measurement error for all possible scores; and it is difficult to equate different tests developed using CTT. Recently, a new approach to scale development has appeared, called item response theory (IRT), which overcomes all of these problems and, in certain cases, results in a scale with true interval-level properties. This article is an introduction to IRT. It concludes by discussing why IRT hasn't been adopted more widely, and some of its limitations.
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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 | 0.067 | 0.224 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.009 | 0.013 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.013 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.015 | 0.007 |
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".