This study investigated the efficacy of the lz person fit statistic for detecting aberrant responding with unidimensional pairwise preference (UPP) measures, constructed and scored based on the Zinnes-Griggs (ZG, 1974) IRT model, which has been used for a variety of recent noncognitive testing applications. Because UPP measures are used to collect both "self-" and "other-" reports, I explored the capability of lz to detect two of the most common and potentially detrimental response sets, namely fake good and random responding. The effectiveness of lz was studied using empirical and theoretical critical values for classification, along with test length, test information, the type of statement parameters, and the percentage of items answered aberrantly (20%, 50%, 100%). We found that lz was ineffective in detecting fake good responding, with power approaching zero in the 100% aberrance conditions. However, lz was highly effective in detecting random responding, with power approaching 1.0 in long-test, high information conditions, and there was no diminution in efficacy when using marginal maximum likelihood estimates of statement parameters in place of the true values. Although using empirical critical values for classification provided slightly higher power and more accurate Type I error rates, theoretical critical values, corresponding to a standard normal distribution, provided nearly as good results.

No description available.