The Pain Catastrophizing Scale (PCS; Sullivan et al., Psychol. Assess. 7, 524-532, 1995) has recently been developed to assess three components of catastrophizing: rumination, magnification, and helplessness. We conducted three studies to evaluate the factor structure, reliability, and validity of the PCS. In Study I, we conducted principal-components analysis with oblique rotation to replicate the three factors of the PCS. Gender differences on the original PCS subscales were also analyzed. In Study II, we conducted confirmatory factor analyses to evaluate the adequacy of fit of four alternative models. We also evaluated evidence for concurrent and discriminant validity. In Study III, we evaluated the ability of the PCS and subscales to differentiate between the responses of clinic (students seeking treatment) and nonclinic undergraduate samples. Also, in the clinic sample, we evaluated evidence of concurrent and predictive validity for the PCS. The internal consistency reliability indices for the total PCS and subscales were examined in all three studies. Limitations and future directions are discussed.

From this study it appears that the Novaco Anger Scale is able to discriminate between clinical and non-clinical populations. These data offer further support to the validity of the Novaco Anger Scale and its use in clinical assessment.

Standard setting methods such as the Angoff method rely on judgments of item characteristics; item response theory empirically estimates item characteristics and displays them in item characteristic curves (ICCs). This study evaluated several indexes of rater fit to ICCs as a method for judging rater accuracy in their estimates of expected item performance for target groups of test-takers. Simulated data were used to compare adequately fitting ratings to poorly fitting ratings at various target competence levels in a simulated two stage standard setting study. The indexes were then applied to a set of real ratings on 66 items evaluated at 4 competence thresholds to demonstrate their relative usefulness for gaining insight into rater "fit." Based on analysis of both the simulated and real data, it is recommended that fit indexes based on the absolute deviations of ratings from the ICCs be used, and those based on the standard errors of ratings should be avoided. Suggestions are provided for using these indexes in future research and practice.

This study compares the efficacy of different strategies for translating item-level, proportion-correct standard-setting judgments into a y-metric test cutoff score for use with item response theory (IRT) scoring, using Monte Carlo methods. Simulated Angoff-type ratings, consisting of 1,000 independent 75 Item × 13 Rater matrices, were generated at five points along the y continuum, at three levels of rater fit to the item characteristics curves, yielding 14,625,000 ratings as the basis of the analyses. These simulated proportion-correct ratings were converted to the IRT y scale using test-level and item-level methods explicated by Kane (1987). Kane's optimally weighted, item-level conversion method initially produced anomalous results; however, it was discovered that imposing a restriction on the weights avoided these anomalies and rendered the optimally weighted method the most statistically efficient. Six areas for future research are outlined for advancing the integration of these classical standard-setting ratings into IRT methodology.

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