| MEASUREMENT PROPERTIES | |
| Limitations | <5 European languages (English) |
| Observations | |
| 1. RELIABILITY | |
| A. Internal Consistency | Tested |
| Cronbach's (Describe) |
Results indicate strong internal consistency reliability for the full Pain Resilience Scale (α = 0.93), as well as its subscales of “Cognitive/Affective Positivity” (α = 0.91) and “Behavioral Perseverance” (α= 0.87) in the second half of the sample. |
| B. Reliability intraobserver or test-retest | Tested |
| Continuous scores: intraclass correlation coefficient (ICC) Dichotomus: Cohen kappa (Describe) |
In addition to relatively consistent group means, computation of the ICC indicated acceptable levels of stability over a one month interval for the full scale (ICC = 0.80), the “Behavioral Perseverance” subscale (ICC = 0.78), and the “Cognitive/Affective Positivity” subscale (ICC =0.77). |
| 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 | Tested |
| Expert opinion (relevance and comprehensiveness) (Describe) |
The Pain Resilience Scale proved to be a better predictor of ischemic pain sensitivity than general measures of dispositional resilience, providing empirical support for the use of a pain-specific resilience measure for pain-free individuals in the context of acute pain. |
| B. Construct Validity: Structural validity |
Tested |
| Hypotheses-testing | Tested |
| Cross-cultural validity | Tested |
| Brief Description |
Using the pattern matrix, items with a loading of less than 0.6 on a factor or cross-loadings of greater than 0.3 were eliminated from the scale. The remaining two-factor solution included 14 items, with 5 items loading on the first factor (“Behavioral Perseverance”) and accounting for 5.17% of the variance and 9 items loading on the second factor (“Cognitive/Affective Positivity”) and accounting for 43.69% of the variance. The correlation between the factors was r = 0.68. The two-factor model provided a strong fit to the data (i.e., CFI = 0.972; RMSEA = 0.049; SRMR = 0.031). |
| C. Criterion validity | Tested |
| Comparison with a 'gold standard' Continuous scores: correlations, ROC curves Dichotomus: Sensitivity & Specificity (Describe) |
All unconditional growth curve models showed that the pain ratings changed significantly over time, with an examination of the AIC and BIC indicating that the cubic growth curve (AIC = 11093.1; BIC = 11109.6) provided a better fit to the data than any other curve (linear AIC = 12553.3, BIC = 12563.6; quadratic AIC = 11297.0, BIC = 11311.4; logarithmic AIC = 12031.4, BIC = 12041.7). Fit indices for the conditional growth curve model incorporating the Pain Resilience Scale (AIC = 11085.7; BIC = 11110.5) were lower than indices for the models incorporating either the Connor-Davidson Resilience Scale (AIC = 11099.7; BIC = 11124.4) or the Brief Resilience Scale (AIC = 11095.9; BIC = 11120.6), indicating that the model including the Pain Resilience Scale provided the best fit to the data. |