Gareth J Griffith1, Kelvyn Jones2. 1. School of Geographical Sciences, University Road, University of Bristol, BS8 1SS, United Kingdom. Electronic address: g.griffith@bristol.ac.uk. 2. School of Geographical Sciences, University Road, University of Bristol, BS8 1SS, United Kingdom.
Abstract
BACKGROUND: Mental health and its complexity, measurement and social determinants are increasingly important avenues of research for social scientists. Quantitative social science commonly investigates mental health as captured by population screening metrics. One of the most common of these metrics is the 12-Item General Health Questionnaire (GHQ-12). Despite itscanonical use as an outcome of interest in social science, the traditional use of the summed scores of summed questionnaires carries empirical and substantive assumptions which are often not fully considered or justified in the research. We outline the implications of these assumptions and the restrictions imposed by traditional modelling techniques and advocate for a more nuanced approach to population mental health modelling and inference. DATA & METHODS: We use novel Exploratory Structural Equation Modelling (ESEM) on a large, representative UK sample taken from the first wave of the Understanding Society Survey, totalling 40,452 respondents. We use this to exemplify the potential of traditional, restrictive assumptions to bias conclusions and policy recommendations. RESULTS: ESEM analysis identifies a 4-factor structure for the GHQ-12, including a newly proposed "Emotional Coping" dimension. This structure is then tested against leading proposed factor structures from the literature and is demonstrated to perform better across all metrics, under both Maximum Likelihood and Bayesian estimation. Moreover, the proposed factors are more substantively dissimilar than those retrieved from previous literature. CONCLUSIONS: The results highlight the inferential limitations of using simple summed scores as population health outcomes. We advocate for the use of the highlighted methods, which in combination with population studies offer quantitative social scientists the opportunity to explore predictors and patterns of underlying processes of population mental health outcomes, explicitly addressing the complexity and measurement error inherent to mental health analysis.
BACKGROUND: Mental health and its complexity, measurement and social determinants are increasingly important avenues of research for social scientists. Quantitative social science commonly investigates mental health as captured by population screening metrics. One of the most common of these metrics is the 12-Item General Health Questionnaire (GHQ-12). Despite itscanonical use as an outcome of interest in social science, the traditional use of the summed scores of summed questionnaires carries empirical and substantive assumptions which are often not fully considered or justified in the research. We outline the implications of these assumptions and the restrictions imposed by traditional modelling techniques and advocate for a more nuanced approach to population mental health modelling and inference. DATA & METHODS: We use novel Exploratory Structural Equation Modelling (ESEM) on a large, representative UK sample taken from the first wave of the Understanding Society Survey, totalling 40,452 respondents. We use this to exemplify the potential of traditional, restrictive assumptions to bias conclusions and policy recommendations. RESULTS: ESEM analysis identifies a 4-factor structure for the GHQ-12, including a newly proposed "Emotional Coping" dimension. This structure is then tested against leading proposed factor structures from the literature and is demonstrated to perform better across all metrics, under both Maximum Likelihood and Bayesian estimation. Moreover, the proposed factors are more substantively dissimilar than those retrieved from previous literature. CONCLUSIONS: The results highlight the inferential limitations of using simple summed scores as population health outcomes. We advocate for the use of the highlighted methods, which in combination with population studies offer quantitative social scientists the opportunity to explore predictors and patterns of underlying processes of population mental health outcomes, explicitly addressing the complexity and measurement error inherent to mental health analysis.
Authors: Sarah Susanne Lütke Lanfer; Ruth Pfeifer; Claas Lahmann; Alexander Wünsch Journal: Int J Environ Res Public Health Date: 2022-08-06 Impact factor: 4.614
Authors: Cristina Mazza; Eleonora Ricci; Daniela Marchetti; Lilybeth Fontanesi; Serena Di Giandomenico; Maria Cristina Verrocchio; Paolo Roma Journal: Int J Environ Res Public Health Date: 2020-08-27 Impact factor: 3.390