Liz Twigg

Abstract: This chapter begins by outlining the conceptual motivation behind multilevel analyses and by identifying a core set of research questions that this approach addresses. It then introduces the idea of multilevel structures and discusses simple and complex multilevel models. It emphasizes that the key strength of multilevel models lies in modeling heterogeneity at different levels and shows how multilevel models can be extended to additional contextual levels (e.g., neighborhoods nested within regions). The estimation procedures underlying such models are discussed, showing how a multilevel framework can provide a general, unified approach to data analysis and how this can be achieved by extensions to the basic hierarchical structure of individuals nested within contexts. The chapter concludes with a discussion of issues that researchers should be aware of when applying multilevel methods.

TL;DR: The GHQ-12 measures both positive and negative aspects of mental health, and although correlated, these dimensions have some independence, and could be used to measure positive mental health in population-based research.

TL;DR: The overall conclusion is that there is little support, at this spatial scale, for the proposition that area measures of social capital exert a beneficial effect on health outcomes.

TL;DR: There may be multiple pathways linking socioeconomic inequalities and ill health, and the effects of place of residence on mental health are greatest among those who are economically inactive and hence more likely to spend the time at home.

TL;DR: The paper concludes that the method is better at estimating smoking than drinking but that it offers a feasible, cheap and more informative alternative to the survey approach to the generation of information on smoking and drinking behaviour.