In latent scaling applications, such as the positioning of political parties, differential item functioning (DIF) may occur because of measurement issues or because of substantive differences in the association between latent and manifest variables. While the first source of DIF has received considerable attention, the second has not, although it is of potential interest to comparative scholars. In this research note, we introduce a novel hierarchical Bayesian item response model that allows us to disentangle different sources of DIF. Drawing on the 2019 Chapel Hill Expert Survey (CHES), we highlight how the same issues are unequally politicized across Western Europe, and how some issues are less ideologically determined than others. Our model can be adapted to alternate settings, allowing researchers to shine a light on variation in, e.g., ideology, issue politicization, or party competition.
Conventional multidimensional statistical models of roll call votes assume that legislators’ preferences are additively separable over dimensions. In this article, we introduce an item response model of roll call votes that allows for non-separability over latent dimensions. Conceptually, non-separability matters if outcomes over dimensions are related rather than independent in legislators’ decisions. Monte Carlo simulations highlight that separable item response models of roll call votes capture non-separability via correlated ideal points and higher salience of a primary dimension. We apply our model to the U.S. Senate and the European Parliament. In both settings, we find that legislators’ preferences over two basic dimensions are non-separable. These results have general implications for our understanding of legislative decision-making, as well as for empirical descriptions of preferences in legislatures.
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