Latent variable modelling of personality-health associations: measures, models and extensions
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Functional health status, morbidity and mortality are determined partly by health behaviours (World Health Organization, 2002), which have determinants of their own. Personality traits, such as Conscientiousness, have a strong association with health behaviours (Bogg & Roberts, 2004). There is a less consistent and generally weaker association between traits and health outcomes (e.g. Neuroticism and mortality). The central problem in this thesis is how to measure, model, maximize, and extend trait-health associations. Conceptual issues associated with modelling traits and health are discussed in chapter one. The next three chapters concern such measurement issues about: personality traits (chapter two), health behaviours (chapter three) and health outcomes, with particular reference to functional health status (chapter four). These chapters are followed by a move to modelling (chapter five), with particular reference to the generalized latent variable modelling (LVM) framework (Muth´en & Muth´en, 1998–2007). The HAPPLE study is introduced (chapter six) which is used to model associations between Conscientiousness and health criteria within the LVMframework (chapter seven). Moving beyond self-reported outcomes, which are a mono-method approach, the role of multiple health behaviours in predicting cardiovascular mortality is considered (chapter eight). In a third section, cortisol is introduced, which is a biomarker of stress reactivity. The diurnal profile of cortisol output is described (chapter nine). Latent growth curve modelling is used to illustrate its association with Neuroticism, in a sample of student volunteers (chapter 10). Taken together, the results highlight the need for a general framework of modelling techniques, in personality-health research. I conclude that biopsychosocial models with excellent explanatory power, which are still parsimonious, can be achieved with LVM and its extensions. However, trait researchers will need to state more clearly the intended destinations of their work in order to attract contributions from, and share knowledge with, other disciplines.