About the Project
Social Science: Sociology
Research on family and social support more widely recognises the importance of space and place. It is well known from neighbourhood studies that youth living in disadvantaged and remote areas are more likely to experience social isolation. However, most studies have examined the role of spatial contexts (neighbourhoods, schools) and largely ignored the personal network context, possibly leading to misattribution errors. Social network research is, in turn, often limited to the analysis of physical distance and has generally failed to consider that both networks and spatial environments affect opportunities for social support.
The objective of this PhD project is to bridge this divide and examine how support from personal ties varies with both networks and residential areas applying a cross-classified multilevel modelling approach with personal (or egocentric) network data. The approach is used to analyse a large survey sample of young adults aged 18-20 living in Switzerland (n=40,000, data collection in 2020-22), including the full national cohort of young men of this age, where the outcome variable of interest is whether the tie (unit of analysis) from an alter to the young adult (ego) is supportive in terms of information provision, advice, emotional help or role model.
The central premise is that support not only hinges on individual or tie characteristics, but also on the properties of personal networks (e.g. density) and areas (e.g. deprivation) where egos and alters live. A three-level modelling strategy can simultaneously investigate ego/ego-network (level 2) and area (level 3) dependences. Cross-classified multilevel models (CCMM) are well suited for handling data that are not strictly hierarchical, since all alters of an ego do not necessarily live within the same area. Drawing on recent advances in multilevel modelling with network data, the project also aims to determine the best ways of modelling young adults’ health and social behaviours and outcomes.
The student will join the community of quantitative social scientists in the School of Social and Political Science (SSPS) at the University of Edinburgh and in the Social and Public Health Sciences Unit (SPHSU) at the University of Glasgow. They will have access to the training provided through the Edinburgh and Glasgow Q-Step Centres, AQMeN (Applied Quantitative Methods Network), the Edinburgh Centre for Statistics and Research Training Centre. The student will complete postgraduate courses on multilevel modelling, social network analysis and statistical modelling with network data.
Applicants must meet the following eligibility criteria:
- A good first degree (2:1 or above) in a social science discipline (e.g. sociology, human geography) or in social statistics
- Demonstrate an interest in social network analysis, advanced statistical modelling for network data, social support, youth inclusion and the role of space and place in social inclusion
- Have a good grounding in quantitative social science methods. Any prior knowledge of social network analysis and multilevel modelling would be desirable
- Demonstrate a good ability to work as part of an international research team and multidisciplinary collaborative project
Please note that all applicants must also meet the ESRC eligibility criteria. ESRC eligibility information can be found here.
For full details and to apply for this studentship, please visit the Scottish Graduate School of Social Science (SGSSS) website here.
Applications will be ranked by a selection panel and applicants will be notified if they have been shortlisted for interview by 7th April 2021. Interviews will take place on 12th April 2021.
All scholarship awards are subject to candidates successfully securing admission to a PhD programme within the University of Edinburgh. Successful scholarship applicants will be invited to apply for admission to the relevant PhD programme after they are selected for funding.
• An annual maintenance grant (stipend)
• Fees at the standard institutional home rate
• Students can also draw on a pooled Research Training Support Grant (RTSG)
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