Primary and Secondary Variants of Psychopathic Traits in at-Risk Youth: Links with Maltreatment, Aggression, and Empathy

The current study examined whether two variants of psychopathic traits (PT) were identifiable in high-risk youth who had not yet been identified as antisocial, some of whom had documented histories of maltreatment (N = 167, M_age = 14.84), and then whether the variants differed in levels of aggression and empathy. High-PT youth with low anxiety and trauma (i.e., primary variant PT) and high anxiety and trauma (i.e., secondary variant PT) were differentiated. The secondary variant group was comprised largely of youth with documented histories of maltreatment. This group of youth also reported higher levels of proactive and reactive aggression than did the primary variant youth and low-PT youth. All youth reported similar levels of affective empathy and only small differences in cognitive empathy emerged: Primary variant youth reported lower cognitive empathy than low-PT youth. Findings support generalization of two variant groups of youth with psychopathic traits to diverse, high-risk samples not already identified as antisocial and have important implications for policy and practice.

MplusAutomation: An R Package for Facilitating Large-Scale Latent Variable Analyses in Mplus

MplusAutomation is a package for R that facilitates complex latent variable analyses in Mplus involving comparisons among many models and parameters. More specifically, MplusAutomation provides tools to accomplish three objectives: to create and manage Mplus syntax for groups of related models; to automate the estimation of many models; and to extract, aggregate, and compare fit statistics, parameter estimates, and ancillary model outputs. We provide an introduction to the package using applied examples including a large-scale simulation study. By reducing the effort required for large-scale studies, a broad goal of MplusAutomation is to support methodological developments in structural equation modeling using Mplus.

Mixture modeling methods for the assessment of normal and abnormal personality, part I: cross-sectional models

Over the past 75 years, the study of personality and personality disorders has been informed considerably by an impressive array of psychometric instruments. Many of these tests draw on the perspective that personality features can be conceptualized in terms of latent traits that vary dimensionally across the population. A purely trait-oriented approach to personality, however, might overlook heterogeneity that is related to similarities among subgroups of people. This article describes how factor mixture modeling (FMM), which incorporates both categories and dimensions, can be used to represent person-oriented and trait-oriented variability in the latent structure of personality. We provide an overview of different forms of FMM that vary in the degree to which they emphasize trait- versus person-oriented variability. We also provide practical guidelines for applying FMM to personality data, and we illustrate model fitting and interpretation using an empirical analysis of general personality dysfunction.

Implementation and development of federally-funded systems of care over time

This study describes development in federally funded systems of care. Data for this study were collected using the System of Care Assessment that rated grantees' enactment of system of care principles in the infrastructure and service delivery domains. Data were collected by trained raters who conducted several site visits over the funding period. This study described system development over time across 61 sites and tested whether gains were statistically significant. Latent profile analysis was used to explore whether sites could be meaningfully grouped based on their baseline service delivery domain scores. Differences across groups were tested in terms of community, system, and client characteristics. Differential growth across groups was also examined. Overall, systems of care developed over time in both the infrastructure and service delivery domains. Although infrastructure scores were generally lower than service delivery scores, greater gains were seen for the infrastructure domain. Three groups of sites were identified that could be distinguished in terms of degree of development overall, and for specific system of care principles. The groups of sites differed in terms of community factors, system features, and client characteristics. In addition, repeated measures analyses found differential growth in system development over time across groups.