Internal consistency reliability is a poor predictor of responsiveness
Bibliographic record
Abstract
BACKGROUND: Whether responsiveness represents a measurement property of health-related quality of life (HRQL) instruments that is distinct from reliability and validity is an issue of debate. We addressed the claims of a recent study, which suggested that investigators could rely on internal consistency to reflect instrument responsiveness. METHODS: 516 patients with chronic obstructive pulmonary disease or knee injury participating in four longitudinal studies completed generic and disease-specific HRQL questionnaires before and after an intervention that impacted on HRQL. We used Pearson correlation coefficients and linear regression to assess the relationship between internal consistency reliability (expressed as Cronbach's alpha), instrument type (generic and disease-specific) and responsiveness (expressed as the standardised response mean, SRM). RESULTS: Mean Cronbach's alpha was 0.83 (SD 0.08) and mean SRM was 0.59 (SD 0.33). The correlation between Cronbach's alpha and SRMs was 0.10 (95% CI -0.12 to 0.32) across all studies. Cronbach's alpha alone did not explain variability in SRMs (p = 0.59, r2 = 0.01) whereas the type of instrument was a strong predictor of the SRM (p = 0.012, r2 = 0.37). In multivariable models applied to individual studies Cronbach's alpha consistently failed to predict SRMs (regression coefficients between -0.45 and 1.58, p-values between 0.15 and 0.98) whereas the type of instrument did predict SRMs (regression coefficients between -0.25 to -0.59, p-values between <0.01 and 0.05). CONCLUSION: Investigators must look to data other than internal consistency reliability to select a responsive instrument for use as an outcome in clinical trials.
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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 | 0.100 | 0.241 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".