Centre for Multilevel Modelling

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Substantive research (LEMMA 3)

Lemma logoWe are working on three substantive research projects:

Change in socio-economic environment and change in physical and mental function over time

(in collaboration with Professor Mel Bartley and Professor David Blane, International Centre for Lifecourse Studies)

Using the BHPS we will examine the relationships between changes in individuals’ physical and mental functioning over time and changes in their socio-economic circumstances (employment status and income). Mental function is measured by Goldberg’s General Health Question (GHQ) which indicates both depression and anxiety and is known to be highly reactive to current circumstances (Wiggins et al., 2004). Previous research has suggested that there is no social gradient in GHQ (Stansfield & Marmot, 1992); it is hypothesized that members of less advantaged social groups are more ‘at risk’ of some improvement in circumstances whereas more advantaged groups over-react to adverse change. We will examine the relationship between change in socio-economic circumstances and change in the GHQ. Physical function is measured by the Activities of Daily Living (ADL) scale which is mostly relevant to the older population, and is therefore highly relevant for policy in an ageing population. We will investigate the extent or speed of change in ADL, and how these may react to changes in social circumstances.

Both GHQ and ADL have been measured annually in the BHPS since 1992. Possible indicators of socio-economic circumstances (SEC) are income (also measured annually) and employment status. We will develop multiprocess models to analyse, simultaneously, (i) GHQ and SEC for the adult population, and (ii) GHQ, ADL and SEC for the older population.

References

Modelling the determinants and consequences of occupational stress with repeatedly assessed real time data

(in collaboration with Professor Derek Johnston and team, Aberdeen Health Psychology Group)

There are two obvious questions in stress research: what causes stress and what are its effects? The dominant models of the determinants of occupational stress are Karasek’s (1979) demand control model that stress is caused by high demand combined with low control, and Siegrist’s (1996) rather similar model that high effort (which is akin to demand) combined with low reward is stressful. Both models have been studied extensively using questionnaire measures. However, many aspects of human behaviour and experience vary continuously and cannot be captured by infrequently administered questionnaires. This has led to the use of computer-based diaries that sample experience in real time, using ecological momentary assessment.

The effects of stress are thought to be ubiquitous, affecting performance and physical and mental health. Pathways to physical health are believed to work through the cumulative impact of stress on physiological arousal. It is now possible to reliably monitor physiological markers (e.g. heart rate) in the natural environment; thus, its relationship with ongoing behaviour and experience, as well as its behavioural consequences, can be assessed. In the past simple regression methods were applied to average measures of the level and variability of behavioural responses and physiological arousal (Jain et al., 1998, Johnston et al., 1994) although more recently multilevel modelling has been used to explore the relationships between behaviour and physiological responding (Johnston et al., 2008, Kamarck et al., 2003, Zanstra et al., 2010).

Using real-time information on stress, its determinants and possible consequences, in combination with traditional retrospective measures, we will model simultaneously the long-term and immediate determinants of stress and its consequences using data on stress in nurses.

References

The effects of children's complex social environments on educational outcomes

Children grow up in complex social environments. The family, neighbourhood and school in which a child develops are often singled out as being particularly important. Knowledge of the relative importance of these different social contexts helps inform decisions about the allocation of resources to programmes and policies. Rasbash et al. (2010) assessed the relative importance of families, neighbourhoods and schools for children’s educational progress. Using English data, they showed family context to be considerably more important than school and neighbourhood contexts.

We will build upon the substantive research of Rasbash et al. (2010) by using data on children’s degree of genetic relatedness (i.e. whether siblings are identical twins, non-identical twins, full siblings, half siblings and so on) to incorporate ideas from behavioural genetics into our research. We will decompose the combined family effects specified in Rasbash et al. into separate genetic and environmental influences. This will provide insight into the relative importance of the “nature” and “nurture” effects of the family. We will then extend our analyses to explore how children’s outcomes at a point in time relate not just to their current environmental contexts, but also to their complete histories of family, neighbourhood and school moves. We hypothesize that ignoring these changes in children’s environments will bias the measured importance of children's complex social environments on their educational outcomes.

References

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