Rasch analysis provides new insights into the measurement properties of the neck disability index
Bibliographic record
Abstract
OBJECTIVE: The most widely used neck-specific measure in intervention trials is the 10-item Neck Disability Index (NDI), which is assumed to be a unidimensional interval scale, as shown by how NDI data are scored, analyzed, and interpreted. Our objective was to use modern measurement methods to test this assumption (and thereby to also test the validity of calculating summed scores and parametric statistics on NDI data) through Rasch analysis. METHODS: NDI data from 521 trial subjects with neck pain were fit to the Rasch model. We examined threshold ordering of NDI items, fit of data to model expectations, presence of differential item functioning, and whether or not the set of NDI items collectively measure a single construct, which is a requirement for calculating summative scores. RESULTS: There was a lack of fit of data to the Rasch model (chi(2) = 140.35, 70 df; P < 0.001). Five items (personal care, lifting, headaches, work, and recreation) had disordered response thresholds. Differential item functioning was detected for age and sex. The NDI items did not contribute to a single construct. Unidimensionality and interval scaling were achieved by removing 2 of the 10 items (resulting in the NDI-8), and converting NDI-8 ordinal (paper) summative scores to NDI-8 interval (Rasch-weighted) scores. CONCLUSION: As originally proposed and conventionally used, the NDI is not a unidimensional scale, and has only ordinal scaling. This raises fundamental doubts about the practice of calculating change scores and other parametric statistics on NDI data. A revised 8-item version provides unidimensional interval-level measurement of neck pain disability.
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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 | 0.074 | 0.227 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".