Interpreting results from Rasch analysis 1. The “most likely” measures coming from the model
Bibliographic record
Abstract
Through Rasch's theory and statistical analysis, scores are transformed and tested for their capacity to respect fundamental measurement axioms. Rasch analysis returns the linear measure of the person's property ("ability") and the item's calibrations ("difficulty"), concealed by the raw scores. The difference between a person's ability and item difficulty determines the probability that a "pass" response is observed. The discrepancy between observed scores and the ideal measures (i.e., the residual) invites diagnostic reasoning. In a companion article, advanced applications of Rasch modelling are illustrated. Implications for rehabilitationQuestionnaires' ordinal scores are poor approximations of measures. The Rasch analysis turns questionnaires' scores into interval measures, provided that its assumptions are respected.Thanks to the Rasch analysis, accurate measures of independence, pain, fatigue, cognitive capacities and other whole person's variables of paramount importance in rehabilitation are available.The current work is addressed to rehabilitation professionals looking for an introduction to interpreting published results based on Rasch analysis.The first of a series of two, the present article illustrates the most common graphic and numeric outputs found in published papers presenting the Rasch analysis of questionnaires.
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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 | 0.101 | 0.420 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".