Psychometric properties of the Insomnia Catastrophizing Scale (ICS) in a large community sample

ABSTRACT

The purpose was to investigate the psychometric properties of the Insomnia Catastrophizing Scale (ICS) including factorial validity and internal consistency as well as discriminative and convergent validity. Associations with sleep parameters and daytime impairment are also examined. Drawn from a randomly selected sample of the general population, 1615 participants completed a survey on insomnia-related nighttime and daytime symptoms, health outcomes and psychological processes, including the ICS. A one-factor solution was supported for both the nighttime catastrophizing (11 items) and daytime catastrophizing (6 items) subscales. Both subscales displayed high internal consistencies (α > 0.90) and accounted for 59.1–70.1% of the variance. The insomnia disorder group had significantly higher scores than participants without insomnia on the two subscales and on the individual items. Cutoffs were established for both subscales with acceptable sensitivity and specificity. Both subscales displayed adequate convergent validity with measures indexing worry, cognitive pre-sleep arousal and anxiety. The two subscales were also significantly associated with nighttime and daytime insomnia symptoms. The ICS is a reliable and valid scale for the assessment of insomnia-related catastrophizing. Future research is needed to examine the test-retest reliability and treatment sensitivity of the ICS.     

Context-sensitive heuristics in statistical reasoning

Previous work has shown that people use anchor-and-adjust heuristics to forecast future data points from previous ones in the same series. We report three experiments that show that they use different versions of this heuristic for different types of series. To forecast an untrended series, our subjects always took a weighted average of the long-term mean of the series and the last data point. In contrast, the way that they forecast a trended series depended on the serial dependences in it. When these were low, people forecast by adding a proportion of the last difference in the series to the last data point. When stronger serial dependences made this difference less similar to the next one, they used a version of the averaging heuristic that they employed for untrended series. This could take serial dependences into account and included a separate component for trend. These results suggest that people use a form of the heuristic that is well adapted to the nature of the series that they are forecasting. However, we also found that the size of their adjustments tended to be suboptimal. They overestimated the degree of serial dependence in the data but underestimated trends. This biased their forecasts.