Performance of Nonparametric Person-Fit Statistics with Unfolding versus Dominance Response Models

Abstract

ABSTRACT Person-fit analyses are commonly used to detect aberrant responding in self-report data. Nonparametric person fit statistics do not require fitting a parametric test theory model and have performed well compared to other person-fit statistics. However, detection of aberrant responding has primarily focused on dominance response data, thus the effectiveness of person-fit statistics in detecting different aberrant behaviors in ideal point data is unclear. This study compares the performance of nonparametric person-fit statistics in unfolding and dominance model contexts. Results for dominance data indicate that increases in detection rates depend, among other factors, on type of aberrant responding and person-fit statistic used. The detection of aberrant responses in ideal point data was ineffective using four nonparametric person-fit statistics, with slightly higher type I error and power less than 0.25. Additional research is needed to identify or develop nonparametric or parametric person-fit statistics effective for aberrant behavior exhibited in ideal point data.