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Applications of Multilevel Modelling: An Evaluation of the Assumption of No Correlation between Explanatory Variables and Random Effects

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  • Full or part time
    Prof P Lambert
  • Application Deadline
    No more applications being accepted
  • Funded PhD Project (European/UK Students Only)
    Funded PhD Project (European/UK Students Only)

About This PhD Project

Project Description

This project is concerned with a methodological question about how multilevel models are used in applied social research. Multilevel models are important statistical methods that are often used in social science projects. Nevertheless, when multilevel models are applied, they frequently violate a statistical assumption about their specification, namely that the estimated random effects are uncorrelated with explanatory variables (‘NCRX’). If there is a correlation, it has been demonstrated that parameter estimates could be biased and/or inefficient. Although the statistical issues surrounding the NCRX assumption have been demonstrated, there remain many research applications where the assumption is not fully explored, and there are divergences between social science disciplines in how seriously the assumption is taken.

This PhD project will (I) review methodological and applied research literature on the NCRX assumption across social science disciplines, (2) analyse simulated data to explore the importance of the assumption in different contexts, and (3) use the ‘Understanding Society’ secondary social survey (special license versions) to carry out two case studies on socio-economic inequalities by occupations and health inequalities by localities in which the NCRX assumption is likely to be relevant. These two case studies are tailored to complement the applied research interests of the supervisors.

Funding Notes


Eligibility:
Applicants must meet the following essential criteria:
• A very good first degree (at least 2:1) in sociology or a cognate social science, which must include some quantitative research methods training; a mathematics or statistics degree will also be considered for 1+3 applicants if the candidate can also demonstrate some evidence of engagement or training in social sciences. Candidates must have either already obtained this degree, or be clearly on target to do so by June 2020
• Demonstrate an interest in, and knowledge of, statistical analytical methods, secondary data analysis, social stratification and/or health inequality
• Have a good grounding in quantitative research methods and have experience working independently with SPSS, Stata, or R

Students must meet ESRC eligibility criteria. ESRC eligibility information can be found here*: https://esrc.ukri.org/skills-and-careers/doctoral-training/prospective-students/

Award Details:
The scholarship is available as a +3 or a 1+3 programme depending on prior research training. This will be assessed as part of the recruitment process. The programme will commence in September 2020. It includes:
• an annual maintenance grant at the RCUK rate (2020/21 rate: £15,285)
• fees at the standard Home rate (equivalent to approximately £4500 per annum)
• students can also draw on a pooled Research Training Support Grant, usually up to a maximum of £750 per year

https://www.sgsss.ac.uk/studentship/applications-of-multilevel-modelling/

Applications will be ranked by a selection panel and applicants will be notified if they have been shortlisted for interview by 07/Apr/2020. Interviews will take place on 16/Apr/2020 (in person, or by videoconference if a visit is impossible). Candidates who are invited for interview will be asked to give a short presentation to the panel that describes their academic interests and their experience of statistical analyses hitherto, and they will be given a short task to complete on the day.

All scholarship awards are subject to candidates successfully securing admission to a PhD programme within The University of Stirling. Successful scholarship applicants will be invited to apply for admission to the relevant PhD programme after they are selected for funding.

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