Intimate Partner Violence and Mental Health Among Transgender and Gender Diverse Young Adults.

Purpose: Research indicates that multiple forms of intimate partner violence (IPV) may be prevalent among transgender and gender diverse (TGD) people, including general forms of IPV (psychological, physical, sexual), as well as identity-specific forms (transgender-related IPV (T-IPV), identity abuse (IA)). Studies also suggest that IPV is associated with negative mental health outcomes in TGD populations, including depression, anxiety, and posttraumatic stress disorder (PTSD). However, little is known about IPV and its association with mental health among TGD young adults. This is noteworthy, as this is a key developmental period for many TGD individuals. Methods: Thus, the present study sought (1) to estimate the lifetime and past-year prevalence of several forms of general and identity-specific IPV among a sample of (N = 200) TGD young adults in New York City and (2) to assess the associations between IPV with recent symptoms of depression, anxiety, and PTSD. To address the study aims, a cross-sectional quantitative survey was conducted between July 2019 and March 2020. Results: Regarding lifetime IPV, IA was most prevalent (57.0%), followed by sexual (40.0%), physical (38.5%), T-IPV (35.5%), and psychological IPV (32.5%). Regarding past-year IPV, psychological IPV was most common (29.0%), followed by IA (27.5%), physical (20.0%), T-IPV (14.0%), and sexual IPV (12.5%). Results of hierarchical regression models indicate that lifetime IA was related to depression, anxiety, and PTSD, while past-year T-IPV was only associated with depression. Conclusions: Taken together, these findings suggest that IPV is highly prevalent among TGD young adults and that IPV - especially identity-specific forms - warrants additional attention from researchers, healthcare professionals, and policymakers, as it may place this population at risk for negative mental health outcomes.

Comparing the relative fit of categorical and dimensional latent variable models using consistency tests.

A number of recent studies have used Meehl's (1995) taxometric method to determine empirically whether one should model assessment-related constructs as categories or dimensions. The taxometric method includes multiple data-analytic procedures designed to check the consistency of results. The goal is to differentiate between strong evidence of categorical structure, strong evidence of dimensional structure, and ambiguous evidence that suggests withholding judgment. Many taxometric consistency tests have been proposed, but their use has not been operationalized and studied rigorously. What tests should be performed, how should results be combined, and what thresholds should be applied? We present an approach to consistency testing that builds on prior work demonstrating that parallel analyses of categorical and dimensional comparison data provide an accurate index of the relative fit of competing structural models. Using a large simulation study spanning a wide range of data conditions, we examine many critical elements of this approach. The results provide empirical support for what marks the first rigorous operationalization of consistency testing. We discuss and empirically illustrate guidelines for implementing this approach and suggest avenues for future research to extend the practice of consistency testing to other techniques for modeling latent variables in the realm of psychological assessment.

Differentiating Categories and Dimensions: Evaluating the Robustness of Taxometric Analyses.

Interest in modeling the structure of latent variables is gaining momentum, and many simulation studies suggest that taxometric analysis can validly assess the relative fit of categorical and dimensional models. The generation and parallel analysis of categorical and dimensional comparison data sets reduces the subjectivity required to interpret results by providing an objective Comparison Curve Fit Index (CCFI). This study takes advantage of developments in the generation of comparison data to examine the robustness of taxometric analyses to unfavorable data conditions. Very large comparison data sets are treated as populations from which many samples are drawn randomly, placing the method on a firmer statistical foundation and increasing its run-time efficiency. The impressive accuracy of the CCFI was consistent with prior findings and robust across novel manipulations of asymmetry, tail weight, and heterogeneous variances. Analyses, an empirical illustration using Minnesota Multiphasic Personality Inventory (MMPI) hypochondriasis data, and discussion focus on the practical implications for differentiating categories and dimensions.

Simulating Multivariate Nonnormal Data Using an Iterative Algorithm.

Simulating multivariate nonnormal data with specified correlation matrices is difficult. One especially popular method is Vale and Maurelli's (1983) extension of Fleishman's (1978) polynomial transformation technique to multivariate applications. This requires the specification of distributional moments and the calculation of an intermediate correlation matrix such that when data are transformed, the target correlation matrix is reproduced. We present an alternative technique that involves sampling data from specified population distributions and identifying the intermediate correlation matrix through an iterative, trial-and-error process. We provide R program code to implement this technique and show that it can generate data under a wide range of conditions (e.g., with empirical samples, with discrete rather than continuous data, when distributional moments are undefined or outside the boundary conditions of other techniques). This approach could be useful in many contexts, especially Monte Carlo studies of multivariate statistics.