Tim Morris

Areas of Application

  • Social science and genetics
  • Social determinants and inequalities in health and education
  • Neighbourhood effects
  • Residential mobility and migration

Methodological Interests

  • Between and within individual variation
  • Repeat measure longitudinal data
  • Genomewide analysis
  • Multilevel modelling
  • Unobserved confounding

Example PhD Topics

  • Using genetic data to investigate social inequalities in health and education. Genetic data is now routinely collected in studies and offers opportunities for novel analyses into health and educational inequalities. This project will make use of routine collected health or education data (specific areas chosen by the student) and genomewide data from studies such as Understanding Society and the Avon Longitudinal Study of Parents and Children.
  • Bias in residential mobility studies due to cohort attrition. Attrition is commonly used as a throwaway limitation in research studies, but it is not widely understood how patterns of selection into or attrition out of such studies may lead to biased or spurious associations between mobility and outcomes. This project will use data from a range of cohort studies including Understanding Society, the Millennium Cohort Study and the Avon Longitudinal Study of Parents and Children to investigate how complex forms of selection and attrition complicate and bias residential and neighbourhood mobility studies.
  • Use of novel analytical designs and triangulation of evidence to estimate neighbourhood effects in the presence of varying sources of bias and confounding. This could include a systematic review of the literature relating to neighbourhood effects on physical, mental or behavioural health, or a review of available methods.
  • Use of methods to control for complex between and within-individual confounding in mobility & migration studies, including propensity score matching and marginal structural models. This project will have a strong methodological focus and would suit someone with a strong quantitative background.
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