Whereas Lievens and Motowidlo (2016) propose a model of situational judgment test (SJT) performance that removes the "situation" in favor of conceptualizing SJTs as a measure of general domain knowledge, we argue that the expression of general domain knowledge is in fact contingent on situational judgment. As we explain, the evidence cited by Lievens and Motowidlo against a situational component does not inherently exclude the importance of situations from SJTs and does overlook the strong support for a person-situation interaction explanation of behavior. Based on the interactionist literature-in particular, the trait activation theory (TAT) and situational strength literatures-we propose a model that both maintains the key pathways and definitions posited by Lievens and Motowidlo and integrates the situational component of SJTs.Interactionist explanations of work behavior have received increasing attention and support in the employee selection literature and stem from a long history of research on person-situation models of personality (e.g., Mischel, 1968). The ability to evaluate situational demands predicts performance across assessment types, including structured interviews (Melchers, Bösser, Hartstein, & Kleinmann, 2012) and assessment centers (Jansen et al., 2013). Further, the ability to identify criteria for performance evaluation (broadly conceptualized as situational cues) has been posited as a key explanation of the criterion-related validity for selection assessments (Kleinmann et al., 2011). Given the evidence supporting a person-situation interaction account of performance in assessment centers and structured

Job satisfaction researchers typically assume a tripartite model, suggesting evaluations of the job are explained by latent cognitive and affective factors. However, in the attitudes literature, connectionist theorists view attitudes as emergent structures resulting from the mutually reinforcing causal force of interacting cognitive evaluations. Recently, the causal attitudes network (CAN; Dalege et al., 2016) model was proposed as an integration of both these perspectives with network theory. Here, we describe the CAN model and its implications for understanding job satisfaction. We extend the existing literature by drawing from both attitude and network theory. Using multiple data sets and measures of job satisfaction, we test these ideas empirically. First, drawing on the functional approach to attitudes, we show the instrumental-symbolic distinction in attitude objects is evident in job satisfaction networks. Specifically, networks for more instrumental features (e.g., pay) show stable, high connectivity and form a single cluster, whereas networks regarding symbolic features (e.g., supervisor) increase in connectivity with exposure (i.e., job tenure) and form clusters based on valence and cognitive-affective distinction. We show these distinctions result in "small-world" networks for symbolic features wherein affective reactions are more central than cognitive reactions, consistent with the affective primacy hypothesis. We show the practical advantage of CAN by demonstrating in longitudinal data that items with high centrality are more likely to affect change throughout the attitude network, and that network models are better able to predict future voluntary turnover compared with structural equation models. Implications of this exciting new model for research and practice are discussed.

Recent personality neuroscience research in large samples suggests that personality traits tend to bear nullto-small relations to morphometric (i.e., brain structure) regions of interest (ROIs). In this preregistered, two-part study using Human Connectome Project data (N = 1,105), we address the possibility that these null-to-small relations are due, in part, to the "level" (i.e., hierarchical placement) of personality and/or morphometry examined. We used a Five-Factor Model framework and operationalized personality in terms of meta-traits, domains, facets, and items; we operationalized morphometry in terms of omnibus measures (e.g., total brain volume), and cortical thickness and area in the ROIs of the Desikan and Destrieux atlases. First, we compared the patterns of effect sizes observed between these levels using mixed effects modeling. Second, we used a machine learning framework for estimating out-of-sample predictability. Results highlight that personality-morphometry relations are generally null-to-small no matter how they are operationalized. Relatively, the largest mean effect sizes were observed at the domain level of personality, but the largest individual effect sizes were observed at the facet and item level, particularly for the Ideas facet of Openness and its constituent items. The largest effect sizes observed were at the omnibus level of morphometry, and predictive models containing only omnibus variables were comparably predictive to models including both omnibus variable and ROIs. We conclude by encouraging researchers to search across levels of analysis when investigating relations between personality and morphometry and consider prioritizing omnibus measures, which appear to yield the largest and most consistent effects.

Introduction Males and females tend to exhibit small but reliable differences in personality traits and indices of psychopathology that are relatively stable over time and across cultures. Previous work suggests that sex differences in brain structure account for differences in domains of cognition. Methods We used data from the Human Connectome Project (N = 1098) to test whether sex differences in brain morphometry account for observed differences in the personality traits neuroticism and agreeableness, as well as symptoms of internalizing and externalizing psychopathology. We operationalized brain morphometry in three ways: omnibus measures (e.g., total gray matter volume), Glasser regions defined through a multi‐modal parcellation approach, and Desikan regions defined by structural features of the brain. Results Most expected sex differences in personality, psychopathology, and brain morphometry were observed, but the statistical mediation analyses were null: sex differences in brain morphometry did not account for sex differences in personality or psychopathology. Conclusions Men and women tend to exhibit meaningful differences in personality and psychopathology, as well as in omnibus morphometry and regional morphometric differences as defined by the Glasser and Desikan atlases, but these morphometric differences appear unrelated to the psychological differences.

The Sandia Matrices are a free alternative to the Raven’s Progressive Matrices (RPMs). This study offers a psychometric review of Sandia Matrices items focused on two of the most commonly investigated issues regarding the RPMs: (a) dimensionality and (b) sex differences. Model-data fit of three alternative factor structures are compared using confirmatory multidimensional item response theory (IRT) analyses, and measurement equivalence analyses are conducted to evaluate potential sex bias. Although results are somewhat inconclusive regarding factor structure, results do not show evidence of bias or mean differences by sex. Finally, although the Sandia Matrices software can generate infinite items, editing and validating items may be infeasible for many researchers. To aide implementation of the Sandia Matrices, we provide scoring materials for two brief static tests and a computer adaptive test. Implications and suggestions for future research using the Sandia Matrices are discussed.