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Rasch Mixture Models for DIF Detection: A Comparison of Old and New Score Specifications

Hannah Frick (), Carolin Strobl () and Achim Zeileis ()

Working Papers from Faculty of Economics and Statistics, University of Innsbruck

Abstract: Rasch mixture models can be a useful tool when checking the assumption of measurement invariance for a single Rasch model. They provide advantages compared to manifest DIF tests when the DIF groups are only weakly correlated with the manifest covariates available. Unlike in single Rasch models, estimation of Rasch mixture models is sensitive to the specification of the ability distribution even when the conditional maximum likelihood approach is used. It is demonstrated in a simulation study how differences in ability can influence the latent classes of a Rasch mixture model. If the aim is only DIF detection, it is not of interest to uncover such ability differences as one is only interested in a latent group structure regarding the item difficulties. To avoid any confounding effect of ability differences (or impact), a score distribution for the Rasch mixture model is introduced here which is restricted to be equal across latent classes. This causes the estimation of the Rasch mixture model to be independent of the ability distribution and thus restricts the mixture to be sensitive to latent structure in the item difficulties only. Its usefulness is demonstrated in a simulation study and its application is illustrated in a study of verbal aggression.

Keywords: mixed Rasch model; Rasch mixture model; DIF detection; score distribution (search for similar items in EconPapers)
JEL-codes: C38 C52 C87 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm
Date: 2013-12
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