Separation of Traits and Extreme Response Style in IRTree Models: The Role of Mimicry Effects for the Meaningful Interpretation of Estimates

Abstract

Item response tree (IRTree) models are a flexible framework to control self-reported trait measurements for response styles. To this end, IRTree models decompose the responses to rating items into sub-decisions, which are assumed to be made on the basis of either the trait being measured or a response style, whereby the effects of such person parameters can be separated from each other. Here we investigate conditions under which the substantive meanings of estimated extreme response style parameters are potentially invalid and do not correspond to the meanings attributed to them, that is, content-unrelated category preferences. Rather, the response style factor may mimic the trait and capture part of the trait-induced variance in item responding, thus impairing the meaningful separation of the person parameters. Such a mimicry effect is manifested in a biased estimation of the covariance of response style and trait, as well as in an overestimation of the response style variance. Both can lead to severely misleading conclusions drawn from IRTree analyses. A series of simulation studies reveals that mimicry effects depend on the distribution of observed responses and that the estimation biases are stronger the more asymmetrically the responses are distributed across the rating scale. It is further demonstrated that extending the commonly used IRTree model with unidimensional sub-decisions by multidimensional parameterizations counteracts mimicry effects and facilitates the meaningful separation of parameters. An empirical example of the Program for International Student Assessment (PISA) background questionnaire illustrates the threat of mimicry effects in real data. The implications of applying IRTree models for empirical research questions are discussed.