Meta-analytic tests of measurement invariance of internalizing and externalizing psychopathology across common methodological characteristics.

Abstract

Factor analytic models of common mental disorders have been hypothesized to be affected by various methodological features, which could undermine the assumption that Internalizing and Externalizing reflect part of the natural structure of psychopathology. In this study, we addressed this issue by testing whether and how methodological features affect the empirical structure of psychopathology using meta-analytic measurement invariance models of Internalizing and Externalizing across multiple sample characteristics. Published studies estimating factor analytic models from Diagnostic and Statistical Manual of Mental Disorders (DSM) diagnoses were screened. A total of 47 samples (N = 118,966) were included in the meta-analysis. Data were pooled into meta-analytic correlation matrices using random effects models that accounted for sampling variability. Multigroup confirmatory factor analytic models of Internalizing and Externalizing were fit to the pooled matrix of DSM diagnoses to test invariance of the structure, factor loadings, and factor covariance. Results supported partial or full invariance across (a) samples selected versus not selected for psychopathology, (b) diagnoses defined by binary DSM thresholds versus dimensional symptoms counts, and (c) diagnoses based on lifetime versus current symptoms. Tentative analyses indicated noninvariance across samples that made diagnoses using hierarchical exclusion rules versus those that did not. Our study suggests that the Internalizing and Externalizing structure is largely robust to common methodological characteristics thought to impact factor analytic models.

Distinct brain network features predict internalizing and externalizing traits in children and adults

“…Internalizing and externalizing symptoms reflect distinct factors across various mental disorders, irrespective of demographic and collection method [33][34][35][36][37] . Predictive network features are similar across behaviors within the broad categories of mental health 16 .…”

A Developmentally Informed Systematic Review and Meta-Analysis of the Strength of General Psychopathology in Childhood and Adolescence

“…Although we do account for inter-dependency of data given shared data sources and data from the same wave of a study, data from longitudinal studies are thought to provide a stronger test of individual differences in the change of factor strength when compared to cross-sectional data (Ringwald et al, , 2022. In an exploratory analysis, we isolated longitudinal studies to determine whether explained common variance changed as a function of age.…”