This study investigated the efficacy of the l_z person fit statistic for detecting aberrant responding with unidimensional pairwise preference (UPP) measures, constructed and scored based on the Zinnes–Griggs item response theory (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, the capability of l_z to detect two of the most common and potentially detrimental response sets, namely fake good and random responding, was explored. The effectiveness of l_z 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%). It was found that l_z was ineffective in detecting fake good responding, with power approaching zero in the 100% aberrance conditions. However, l_z 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.