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Tests of statistical interactions (or tests of moderation effects) in personality disorder research are a common way for researchers to examine nuanced hypotheses relevant to personality pathology. However, the nature of statistical interactions makes them difficult to reliably detect in many research scenarios. The present study used a flexible, simulation-based approach to estimate statistical power to detect trait-by-trait interactions common to psychopathy research using the Triarchic Model of Psychopathy and the Psychopathic Personality Inventory. Our results show that even above average sample sizes in these literatures (e.g., N = 428) provide inadequate power to reliably detect trait-by-trait interactions, and the sample sizes needed to detect interaction effect sizes in realistic scenarios are extremely large, ranging from 1,300 to 5,200. The implications for trait-by-trait interactions in psychopathy are discussed, as well as how the present findings might generalize to other areas of personality disorder research. We provide recommendations for how to design research studies that can provide informative tests of interactions in personality disorder research, but also highlight that a more realistic option is to abandon the traditional approach when testing for interaction effects and adopt alternative approaches that may be more productive.

We respond to a critique by Patrick et al. (2020) of our recent study (Roy et al., 2020) that raised questions regarding the three-factor model of the Triarchic Psychopathy Measure (TriPM). Roy et al. demonstrated that a replicable model involving seven unidimensional factors accounted for the TriPM items across North American and European general population samples, as well as European male offenders. Despite having access to large TriPM datasets, the Patrick et al. critique relied on tangential analyses of general personality traits, using a single college sample with TriPM data as supplemental. Thus, Patrick et al. ignored findings highlighting multidimensional TriPM scales and the uncertainty they introduce with respect to the larger nomological network of psychopathy. In our reply, we demonstrate additional problems with the three-factor TriPM model and show that the seven-factor model out-performs the three-factor model in predicting correlates of psychopathy. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Recent reviews suggest that, like much of the psychological literature, research studies using laboratory aggression paradigms tend to be underpowered to reliably locate commonly observed effect sizes (e.g., r = ~.10 to .20, Cohen’s d = ~.20 to .40). In an effort to counter this trend, we provide a “power primer” that laboratory aggression researchers can use as a resource when planning studies using this methodology. Using simulation-based power analyses and effect size estimates derived from recent literature reviews, we provide sample size recommendations based on type of research question (e.g., main effect vs. two-way vs. three-way interactions) and correlations among predictors. Results highlight the large number of participants that must be recruited to reach acceptable (~80%) power, especially for tests of interactions where the recommended sample sizes far exceed those typically employed in this literature. These discrepancies are so substantial that we urge laboratory aggression researchers to consider a moratorium on tests of three-way interactions. Although our results use estimates from the laboratory aggression literature, we believe they are generalizable to other lines of research using behavioral tasks, as well as psychological science more broadly. We close by offering a series of best practice recommendations and reiterating long-standing calls for attention to statistical power as a basic element of study planning.

Numerous studies leverage item response theory (IRT) methods to examine measurement characteristics of alcohol use disorder (AUD) diagnostic criteria. Less work has examined the consistency of AUD IRT parameter estimates, an essential step for establishing measurement invariance, making statements about symptom diagnosticity, and validating the theoretical construct. A Bayesian meta-analysis of IRT discrimination values for AUD criteria across 33 independent samples (Total N = 321,998) revealed that overall consistency of AUD criteria discriminations was low (generalized intraclass correlation range = .105-.249). However, specific study characteristics accounted for substantial variability, suggesting that the unreliability is partially systematic. We replicated evidence of differential item functioning (DIF) via established factors (e.g., age, gender), but the magnitudes were small compared with DIF associated with assessment instrument. These results offer practical recommendations regarding which instruments to use when specific AUD criteria are of interest and which criteria are most sensitive when comparing demographic groups.