Incorporating wellbeing into general factor models: A more complete mental state?

This study aimed to understand whether incorporating wellbeing as another dimension within general factor models of mental health is (a) feasible and (b) useful.

Data from two time points (Year 7 and Year 9) for 15258 adolescents who participated in the HeadStart programme in England was used. In Stage 1, we used structural equation modelling on time point 1 data to test different latent variable models incorporating psychopathology and wellbeing dimensions. In Stage 2, we tested whether the latent factors identified in Stage 1 significantly predicted impairment at time point 2.

A general factor model incorporating a shared underlying dimension between (lack of) wellbeing and psychopathology as well as unique specific factors had good fit to the data at Stage 1. Further, although both general factor models with and without wellbeing fit the data well at Stage 1, only the general factor model with wellbeing met all required fit thresholds when regressions to predict impairment were added in. The model without any general factor (correlated factors model) met pre-defined fit thresholds but had lower fit indices.

The incorporation of wellbeing into general factor models may help represent more nuanced mental health states and may be useful in predicting future functional states, however such a model needs further replication with comprehensive measures and comparison with alternative models to verify its validity and utility.