Direct and indirect links between children’s socio-economic status and education: pathways via mental health, attitude, and cognition

Abstract

A child’s socio-economic environment can profoundly affect their development. While existing literature focusses on simplified metrics and pair-wise relations between few variables, we aimed to capture complex interrelationships between several relevant domains using a broad assessment of 519 children aged 7–9 years. Our analyses comprised three multivariate techniques that complimented each other, and worked at different levels of granularity. First, an exploratory factor analysis (principal component analysis followed by varimax rotation) revealed that our sample varied along continuous dimensions of cognition, attitude and mental health (from parallel analysis); with potentially emerging dimensions speed and socio-economic status (passed Kaiser’s criterion). Second, k-means cluster analysis showed that children did not group into discrete phenotypes. Third, a network analysis on the basis of bootstrapped partial correlations (confirmed by both cross-validated LASSO and multiple comparisons correction of binarised connection probabilities) uncovered how our developmental measures interconnected: educational outcomes (reading and maths fluency) were directly related to cognition (short-term memory, number sense, processing speed, inhibition). By contrast, mental health (anxiety and depression symptoms) and attitudes (conscientiousness, grit, growth mindset) showed indirect relationships with educational outcomes via cognition. Finally, socio-economic factors (neighbourhood deprivation, family affluence) related directly to educational outcomes, cognition, mental health, and even grit. In sum, cognition is a central cog through which mental health and attitude relate to educational outcomes. However, through direct relations with all components of developmental outcomes, socio-economic status acts as a great ‘unequaliser’.

Capturing developmental dynamics within a transdiagnostic framework: Challenges and promises

“…A second example methodology is 'idiographic symptom networks'. These are an extension of more commonly used group level network science, in which nodes correspond to particular measures and the edges between them correspond to the partial relationships between them (Dalmaijer et al, 2021). Network analysis, when applied to the group, provides a way of mapping the interactions between multiple symptoms simultaneously (Dalmaijer et al, 2021).…”

“…These are an extension of more commonly used group level network science, in which nodes correspond to particular measures and the edges between them correspond to the partial relationships between them (Dalmaijer et al, 2021). Network analysis, when applied to the group, provides a way of mapping the interactions between multiple symptoms simultaneously (Dalmaijer et al, 2021). The application of graph theory can provide a way of characterising the organisational properties of that network, such as whether particular nodes act as hubs, connecting other more disparate symptoms.…”

Testing the specificity of environmental risk factors for developmental outcomes

“…Data-driven methods are increasingly popular for exploring how environmental risks and developmental outcomes are associated and complement experimental approaches. For example, network models have recently been used to explore associations between adversities and developmental outcomes (Dalmaijer et al, 2021;Sheridan et al, 2020). Here, we introduce a novel exploratory approach, which is particularly appropriate for identifying dimensions of risk factors that best predict dimensions of developmental outcomes.…”

“…Yet, a comprehensive evidence base shows that environmental risk factors are highly co-occurring and predict a broad range of outcomes (Asmussen et al, 2020;Evans et al, 2013). For example, socioeconomic status correlates with a wide range of life outcomes, including cognition, mental health, and educational outcomes (Dalmaijer et al, 2021), and with exposure to many different adverse childhood experiences (ACE; Walsh et al, 2019). Co-occurrence can make detecting specific effects in observational data more difficult, as measuring and accounting for many confounding variables may not be possible.…”

Cognitive difficulties following adversity are not related to mental health: findings from the ABCD study

“…Each account has also been inspired and supported by particular methodological approaches that are hard to tease apart from the theoretical conclusions they were designed to support (Bignardi, Dalmaijer, & Astle, 2022). Data-driven methods are increasingly popular for exploring links between early environmental risks and developmental outcomes (e.g., Bignardi et al, 2022;Carozza et al, 2022;Dalmaijer et al, 2021;Sheridan et al, 2020). These complement experimental approaches and are free from a priori assumptions about the associations that might exist between exposures and outcomes.…”