Social Statistics

Research in the Social Statistics pathway investigates social dynamics in space and time, focussing on ageing and ethnicity in Manchester, population dynamics in Liverpool, and methodological research in criminology and health sciences in Lancaster.

Programmes eligible for NWSSDTP funding

N.B. Master’s programmes can only be funded as part of a 1+3/2+2 Studentship

Lancaster University

University of Liverpool

University of Manchester

For information on how to apply for funding, please visit our How to Apply page.

Pathway Representatives

Contact details for Social Statistics Pathway Representatives at each institution can be found here

Current Social Statistics NWSSDTP students


photo_ESRCEvanthia Koukouli (2018 Cohort)

 

An holistic statistical approach for determining the relationships between social, economic and health markers using the English Longitudinal Study of Ageing

The global elderly population is expected to double by 2050, reaching nearly 2.1 billion. Initiatives to tackle this elderly population inflation are needed in order to sustain society’s well-being and people’s quality of life. We develop methodology aiming to understand the dynamics of the ageing process and the relationships between the factors that affect ageing progression (evidence from the English Longitudinal Study of Ageing).


JJJames Jackson (2019 Cohort)

 

Developing synthetic data methods for administrative databases

The aim of the project is to develop synthetic data methods that are capable of handling large administrative databases, with particular focus on the synthesis of categorical variables. Effective synthetic data methods could provide analysts with access to administrative data, whilst protecting the confidentiality of the individuals included.


Photo_Andrea Aparicio (002)Andrea Lisette Aparicio Castro (2018 Cohort)

 

Modelling and forecasting the spatial and temporal patterns of bilateral international migration flows.

While migration flow data are available from individual countries of South America, they often incomplete and/or incomparable between countries and over time. Thus, there is a need to use different data sources for estimating South American migration flows. However, data sources differ. In order to overcome these differences and the limitations of each data source, my research generalises the Raymer et.al.’s model (2013), which enables the combination of various data sources through a measurement model that corrects for data inadequacies. The resulting outcome will be a set of synthetic annual estimates of migration flows with measures of uncertainty for South American countries from 1990 to 2018.


 

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