Statistic lz-Based Person-Fit Methods for Noncognitive Multiscale Measures

Abstract

Most person-fit statistics require long tests to reliably detect aberrant item-score vectors and are not readily applicable to noncognitive measures that consist of multiple short subscales. The authors propose combining subscale person-fit information to detect aberrant item-score vectors on noncognitive multiscale measures. They used a simulation study and three empirical personality and psychopathology test datasets to assess five multiscale person-fit methods based on the [Formula: see text] person-fit statistic with respect to (a) identifying aberrant item-score vectors, (b) improving accuracy of research results, and (c) understanding causes of aberrant responding. Simulated data analysis showed that the person-fit methods had good detection rates for substantially misfitting item-score vectors. Real-data person-fit analyses identified 4% to 17% misfitting item-score vectors. Removal of these vectors little improved model fit and test-score validity. The person-fit methods helped to understand causes of aberrant responding after controlling for response style on the explanatory variables. More real-data analyses are needed to demonstrate the usefulness of multiscale person-fit methods for noncognitive multiscale measures.