Latent Class Analysis of Mental Health in Middle Childhood: Evidence for the Dual-Factor Model

Abstract

Mental health is complex, comprising both mental distress and well-being. This study used latent class analysis to identify common combinations of mental distress and well-being ('mental health classes') among schoolchildren aged 8-9 years (N = 3340). Thirteen items, measuring a range of conduct problems, emotional symptoms, and subjective well-being, were included in the analysis. Four mental health classes were identified: (1) complete mental health (n = 1895, 57%), (2) vulnerable (n = 434, 13%), (3) emotional symptoms but content (n = 606, 18%), and (4) conduct problems but content (n = 404, 12%). The classes were reliably identified across different datasets, and for males and females. Differential relations with covariates indicated that mental health classes were distinct and externally valid. The results supported the dual-factor model of mental health, suggesting that mental distress and subjective well-being are separate continua. Three of the four possible combinations of high and low distress and subjective well-being posited by the dual-factor model were found using this inductive statistical method. Importantly, our analysis also revealed two 'symptomatic but content' groups, differentiated by symptom domain (internalising/externalising). The covariate analyses between mental health classes and sociodemographic factors, prior academic attainment, school connectedness, and peer support, indicated that there are nuanced relations between those variables and particular constellations of mental distress and well-being. As one of the few dual-factor studies to focus on middle childhood, the current study adds important new evidence that contributes to our understanding of the complexities of mental health among schoolchildren.

Dual-Factor Mental Health from Childhood to Early Adolescence and Associated Factors: A Latent Transition Analysis

“…To investigate dual-factor mental health, researchers have typically identified the subgroups noted above using sample or norm-based cut-off points. Recently, approaches such as latent class analysis (LCA) and latent profile analysis (LPA; Berlin et al, 2013) have been used to identify subgroups of children and adolescents with similar patterns of dual-factor mental health (e.g., Petersen et al, 2020). LCA and LPA are model-based clustering techniques that identify underlying subgroups based on different indicator variables (Collins & Lanza, 2010).…”

Predicting transitions between longitudinal classes of posttraumatic stress disorder, adjustment disorder, and well-being during the COVID-19 pandemic: Protocol of a latent transition model in a general Dutch sample

“…Considering findings on mental health prevalence from cross-sectional studies of COVID-19 pandemic responses [6], we also expect a class of individuals with difficulties adjusting (moderate to high AD symptoms) and moderate/low well-being, but who do not show traumatic responses (low PTSD symptoms; class 3), and a class for whom PTSD symptoms are at the forefront (high PTSD) and moderate/low well-being, but who experience moderate/low adjustment difficulties (class 4) [28,29]. Lastly, in line with the dual continua model, we expect a class that shows elevated AD and PTSD symptomatology while maintaining moderate to high well-being levels (class 5) [17,27].…”

“…LCA categorizes individuals into classes based on similar response patterns. For instance, LCA has been used to show that mental illness and well-being are separate continua; classes were found of people showing high well-being and low psychopathology symptoms, whereas other classes were characterized by people with elevated symptoms while reporting high well-being [27].…”

“…Based on previous studies on individual differences in mental health [22,23,27] we expect to identify at least five classes that differ with respect to symptom-profiles. We expect that the modal response would consist of a subgroup representing complete mental health (i.e., individuals with low PTSD and AD symptomatology and moderate to high well-being) (class 1), based on prior research of symptom trajectories [22,23,27]. Furthermore, we expect to identify a class with severe symptomatology (high AD and PTSD symptoms) and low well-being (class 2) [21,27].…”

“…We expect that the modal response would consist of a subgroup representing complete mental health (i.e., individuals with low PTSD and AD symptomatology and moderate to high well-being) (class 1), based on prior research of symptom trajectories [22,23,27]. Furthermore, we expect to identify a class with severe symptomatology (high AD and PTSD symptoms) and low well-being (class 2) [21,27]. Considering findings on mental health prevalence from cross-sectional studies of COVID-19 pandemic responses [6], we also expect a class of individuals with difficulties adjusting (moderate to high AD symptoms) and moderate/low well-being, but who do not show traumatic responses (low PTSD symptoms; class 3), and a class for whom PTSD symptoms are at the forefront (high PTSD) and moderate/low well-being, but who experience moderate/low adjustment difficulties (class 4) [28,29].…”