The deployment of statistical models—such as those used in item response theory—necessitates the use of indices that are informative about the degree to which a given model is appropriate for a specific data context. We introduce the InterModel Vigorish (IMV) as an index that can be used to quantify accuracy for models of dichotomous item responses based on the improvement across two sets of predictions (i.e., predictions from two item response models or predictions from a single such model relative to prediction based on the mean). This index has a range of desirable features: It can be used for the comparison of non-nested models and its values are highly portable and generalizable. We use this fact to compare predictive performance across a variety of simulated data contexts and also demonstrate qualitative differences in behavior between the IMV and other common indices (e.g., the AIC and RMSEA). We also illustrate the utility of the IMV in empirical applications with data from 89 dichotomous item response datasets. These empirical applications help illustrate how the IMV can be used in practice and substantiate our claims regarding various aspects of model performance. These findings indicate that the IMV may be a useful indicator in psychometrics, especially as it allows for easy comparison of predictions across a variety of contexts.

This article presents new manuscript evidence of one of the most widely read devotional texts of the Middle Ages – the Prayers and meditations (Orationes sive meditationes) of St Anselm, abbot of Le Bec and archbishop of Canterbury – to argue that some of these prayers were originally written and disseminated in the voice of a woman. Composed and copied in grammatically feminine forms, these prayers were, as I show here, deliberately designed to be used and recited by women. This new and hitherto completely overlooked evidence is transformative for our understanding of women's active voices in medieval cultures of devotion.

Several approaches to judgment and decision making emphasize the effort-reducing properties of heuristics. One prominent example for effort-reduction is the recognition heuristic (RH) which proposes that judgments are made by relying on one single cue (recognition), ignoring other information. Our research aims to shed light on the conditions under which the RH is more useful and thus relied on more often. We propose that intuitive thinking is fast, automatic, and effortless whereas deliberative thinking is slower, stepwise, and more effortful. Because effort-reduction is thus much more important when processing information deliberately, we hypothesize that the RH should be more often relied on in such situations. In two city-size-experiments, we instructed participants to think either intuitively or deliberatively and assessed use of the RH through a formal measurement model. Results revealed that, in both experiments, use of the RH was more likely when judgments were to be made deliberatively, rather than intuitively. As such, we conclude that the potential application of heuristics is not necessarily a consequence of “intuitive” processing. Rather, their effort-reducing features are probably most beneficial when thinking more deliberatively.