The funding for the SYLLS project finished and it has continued under the ADRC-S
The England and Wales Longitudinal Study (ONS LS), Scottish Longitudinal Study (SLS) and Northern Ireland Longitudinal Study (NILS) are incredibly rich micro-datasets linking census and other health and administrative data (births, deaths, marriages, cancer registrations) for individuals and their immediate families across several decades. Whilst unique and valuable resources, the sensitive nature of the information they contain means that access to the microdata is restricted to approved researchers and LS support staff, who can only view and work with the data in safe settings controlled by the national statistical agencies. Consequently, compared to other census data products such as the aggregate statistics or interaction data, the three longitudinal studies are used by a small number of researchers – a situation which limits their potential impact.
Given that confidentiality constraints mean that open access is not possible with the real microdata, alternative options were needed to allow academics and other users to carry out their research more freely. To address this the SYLLS project (Synthetic Data Estimation for UK Longitudinal Studies) was set up. SYLLS developed techniques to produce synthetic data which mimiced the real data and preserved the relationships between variables and transitions of individuals over time, but was more freely accessible.
This project, a collaboration between the three UK Longitudinal Study Research Support Units – CeLSIUS, LSCS and the NILS-RSU, and now continuing under the auspices of the ADRC-S, makes use of two complementary methods for generating synthetic data products:
- Statistical modelling with conditional specification is used to generate bespoke synthetic datasets for individual research projects. After developing their analyses on the synthetic data the users will have the option of having them repeated and, we hope, confirmed on the actual LS data sets. Routines to generate a synthetic version of real datasets are implemented in the R package ‘synthpop’. The package and a vignette with a working example are freely available from the R website.
- Microsimulation is used to generate synthetic longitudinal data ‘spines’ for each of the national longitudinal studies. These ‘spines’ synthesise the full sample but include only the most frequently used variables and longitudinal transitions.
The SYLLS team are:
- Prof Mike Batty, Co-Investigator
- Dr Adam Dennett, Principal Investigator
- Prof Chris Dibben, Co-Investigator
- Prof Tony Gallagher, Co-Investigator
- Prof Gillian Raab, Co-Investigator
- Dr Nicola Shelton, Co-Investigator
- Dr Ian Shuttleworth, Co-Investigator
- Rachel Stuchbury, Senior Research Associate
- Dr Beata Nowok, Research Associate
- Dr Belinda Wu, Research Associate