Adapting the Person Fit Analysis: Ideas on Detecting Person Misfit in Computerized Adaptive Testing

Abstract

In this editorial chapter, I aim to summarize findings on person fit analysis in computerized adaptive testing (CAT) from prior research and discuss potential avenues for further research. In item response theory (IRT) applications, person fit quantifies fit of a response pattern to the model (Bradlow & Weiss, 2001, p. 86). Person misfit refers to unexpected response patterns by individuals. There are many potential reasons of misfit including special knowledge (Sinharay, 2016), cheating, guessing (Meijer, 1996), fatigue (Swearingen, 1998), warming up (Meijer, 1996), or faking (Ferrando & Anguiano-Carrasco, 2012). Evaluation of misfit is a significant step for addressing discrepancies within the measurement model. When IRT models are used, evidence of model fit which involves person fit analysis results should be reported (Standard 4.10; AERA, APA & NCME, 2014) as validity evidence to enhance score interpretations. Once misfitting items are identified, corrective steps such as item revision or removal can be implemented. For examinees who exhibit misfit, additional exploration can be undertaken to pinpoint behaviors that might necessitate adjustments to the test program or corrective interventions for particular examinees.