Empirical Comparisons Among Models in Detecting Extreme Response Style

The models that have been proposed within the framework of item response theory (IRT) to identify extreme response style (ERS) may be categorized within three groups. The first group treats ERS as an explicit additional dimension that influences item responses and is distinct from the latent ability that the items intend to measure, e.g., the multidimensional nominal response model (MNRM) for response styles. The second group uses a weighting parameter for the thresholds of each item category to account for individuals’ ERS, for example, the modified generalized partial credit model for ERS (ERS-GPCM). The third group incorporates a tree-like procedure into IRT to differentiate participants’ latent ability from ERS (e.g., the tree model with a dominance model and an ideal-point model). To facilitate the practical use of these approaches, the present study compared the performance of these methods against the conventional IRT. The combined findings of model-fit indexes, estimates of reliability, latent ability, and ERS and the estimated relationship between latent ability and ERS suggested that the MNRM might be a better option to differentiate normal participants from ERS respondents.