| MEASUREMENT PROPERTIES | |
| Limitations | English only |
| Observations | |
| 1. RELIABILITY | |
| A. Internal Consistency | Tested |
| Cronbach's (Describe) |
Cronbach's alpha was 0.74. |
| B. Reliability intraobserver or test-retest | Tested |
| Continuous scores: intraclass correlation coefficient (ICC) Dichotomus: Cohen kappa (Describe) |
The reliability intra-class correlation coefficient (ICC) was 0.91 (95% CI 0.86 to 0.94). |
| C. Reliability interobserver or Measurement error | Not Tested |
| Standard error of measurement (SEM), smallest detectable change (SDC) or Limits of agreement (LoA) (Describe) |
|
| 2. VALIDITY | |
| A. Content validity: face validity | Not Tested |
| Expert opinion (relevance and comprehensiveness) (Describe) |
|
| B. Construct Validity: Structural validity |
Tested |
| Hypotheses-testing | Tested |
| Cross-cultural validity | Not Tested |
| Brief Description |
The summary itemfit statistics from analyses of CAP-Knee scores with four response levels per item indicated mis fit to the Rasch model, with significant item etrait interaction [X 2 (df) 63 (28); p < 0.001]. Principal Components Analysis of the residuals found that all eight items loaded on the first component. Eleven (4%) of 246 t-tests were significant. Four items (neuropathic-like pain, fatigue, anxiety and depression) showed misfit for outfit values in one or more response options. CAP-Knee item residuals demonstrated no correlations (r < 0.3) between items. None of the items exhibited non-uniform DIF for age or sex. None of the items showed uniform-DIF for age, however, the pain distribution item showed uniform-DIF ( P = 0.03) for sex. Factor analysis con firmed the one-factor model (comparative fit index = 0.98; Tucker Lewis Index = 0.97; model X 2 (df) = 38 (20); root mean square error of approximation = 0.08). All eight items loaded significantly on to the single latent factor, which were named ‘Central Mechanisms ’. |
| C. Criterion validity | Not Tested |
| Comparison with a 'gold standard' Continuous scores: correlations, ROC curves Dichotomus: Sensitivity & Specificity (Describe) |
|