This study confronted the classical idea that generalized prejudice is rooted in a cognitive tendency to sort reality into rigid and simple categories with the more recent idea that prejudice is shaped by moral intuitions. In a diverse Swedish sample (N 1⁄4 430), moral absolutism was more strongly associated with generalized prejudice against derogated and dissident (but not dangerous) groups than were other aspects of intolerance of ambiguity. But there was little direct association between any aspect of intolerance of ambiguity and generalized prejudice once indirect relations through binding moral intuitions (which elevated prejudice) and individualizing moral intuitions (which decreased prejudice) had been taken into account. These findings suggest that intolerance of ambiguity is associated with generalized prejudice mainly insofar as it leads to a distinctly moral dichotomization of persons into categories such as insiders and outsiders, law-abiding citizens and deviants, and the righteous and the impure.

The Moral Foundations Questionnaire, consisting of the Relevance subscale and the Judgment subscale, was constructed using the framework of classical test theory for the purpose of measuring five moral foundations. However, so far, no study has investigated the latent properties of the questionnaire. Two independent samples, one from the New Zealand Attitudes and Values Study (N = 3989), and one nationally representative sample from Sweden (N = 1004), were analyzed using Mokken scale analysis and optimal scaling procedure. The results indicate strong shared effects across both samples. Foremost, the Moral Foundations Questionnaire holds two latent trait dimensions, corresponding to the theoretical partitioning between Individualizing and Binding foundations. However, while the Relevance subscale was, in all, reliable in ordering respondents on level of ability, the Judgment subscale was not. Moreover, the dimensionality analysis showed that the Relevance subscale carries three cross-cultural homogeneity outlier items (items for loyalty and disorder concerns) in both samples. Lastly, while the test for local independence indicated adequate fit for the Individualizing trait dimension, the Binding dimension was theoretically ambiguous. Suggestions for improvements and future directions are discussed.

This paper reports a validation study based on descriptive multidimensional item response theory (DMIRT), implemented in the R package D3mirt by using the ERS-C, an extended version of the Relevance subscale from the Moral Foundations Questionnaire including two new items for collectivism (17 items in total). Two latent models are created and investigated in two large samples (N = 1560 and N = 1380 US citizens). The internal investigation indicates a presence of two unidimensions, Compassion and Conformity, and a third within-multidimension of Collectivism in both samples. The external investigation show that Republicans are the highest on Conformity in the models, followed by Independents, and lastly Democrats. Democratic followers are the highest on compassion, followed by Independents and Republicans. No difference between groups on Collectivism were found. Lastly, the models succeeded in correctly identifying the Republican and Democratic voters but struggled to correctly classify the Independent.

The D3mirt package for R (R Core Team, 2021) offers functions for analyzing questionnaire items used in psychological research in a three-dimensional latent space. The application is based on descriptive multidimensional item response theory (DMIRT) (Reckase, 1985, 2009; Reckase & McKinley, 1991), a statistical framework incorporating vector geometry to describe item characteristics. The method is foremost visual, and the latent model can be plotted as an interactive graphical device with the help of a dedicated plot function based on the RGL 3D visualization device system for R (Adler & Murdoch, 2023). Alongside the plot function, the package also includes a model identification function that helps the user identify the DMIRT model and a model estimation function for extracting the necessary vector estimates. New additions to the DMIRT framework introduced in the D3mirt package include studying constructs and individual scores plotted in the three-dimensional latent model.

For identifying, estimating, and plotting descriptive multidimensional item response theory models, restricted to 3D and dichotomous or polytomous data that fit the two-parameter logistic model or the graded response model. The method is foremost explorative and centered around the plot function that exposes item characteristics and constructs, represented by vector arrows, located in a three-dimensional interactive latent space. The results can be useful for item-level analysis as well as test development.