A comparison of the polytomous Rasch analysis output of RUMM2030 and R (ltm/eRm/TAM/lordif)
Bibliographic record
Abstract
BACKGROUND: Patient-reported outcome measures developed using Classical Test Theory are commonly comprised of ordinal level items on a Likert response scale are problematic as they do not permit the results to be compared between patients. Rasch analysis provides a solution to overcome this by evaluating the measurement characteristics of the rating scales using probability estimates. This is typically achieved using commercial software dedicated to Rasch analysis however, it is possible to conduct this analysis using non-specific open source software such a R. METHODS: Rasch analysis was conducted using the most commonly used commercial software package, RUMM 2030, and R, using four open-source packages, with a common data set (6-month post-injury PRWE Questionnaire responses) to evaluate the statistical results for consistency. The analysis plan followed recommendations used in a similar study supported by the software package's instructions in order to obtain category thresholds, item and person fit statistics, measures of reliability and evaluate the data for construct validity, differential item functioning, local dependency and unidimensionality of the items. RESULTS: There was substantial agreement between RUMM2030 and R with regards for most of the results, however there are some small discrepancies between the output of the two programs. CONCLUSIONS: While the differences in output between RUMM2030 and R can easily be explained by comparing the underlying statistical approaches taken by each program, there is disagreement on critical statistical decisions made by each program. This disagreement however should not be an issue as Rasch analysis requires users to apply their own subjective analysis. While researchers might expect that Rasch performed on a large sample would be a stable, two authors who complete Rasch analysis of the PRWE found somewhat dissimilar findings. So, while some variations in results may be due to samples, this paper adds that some variation in findings may be software dependent.
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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 | 0.038 | 0.154 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".