Centre for Longitudinal Studies (K26)The Centre for Longitudinal Studies (CLS) is an Economic and Social Research Council resource centre. It is based at the Department of Social Science, UCL Institute of Education, University of London.
The cohort studies
CLS is responsible for running four of Britain’s internationally-renowned cohort studies:
What we do Running the cohort studies is a complex process, involving a number of different activities. To find out more about what CLS is up to, see our news and events sections. We make the datasets widely available for researchers to use via the UK Data Service, and provide training and support in their use. Our researchers conduct research on a variety of topics that have helped to improve individuals’ lives. Our research covers areas including education and child development, social mobility, health inequalities, mental health and wellbeing, inequalities, families and family life, ageing, and survey and statistical methods. FOR ALL QUERIES PLEASE USE THE CONTACT TABS FOUND IN EACH OF THE INDIVIDUAL COURSES/CONFERENCES AND PRODUCTS, PLEASE ONLY CONTACT THE ONLINE STORE DIRECTLY IF YOU ARE EXPERIENCING PAYMENT DIFFICULTIES. Centre for Longitudinal Studies (K26)K26 Using R for Longitudinal Data AnalysisDescriptionThis is an in-person two-day course for quantitative researchers wanting to perform longitudinal data analyses with the programming language R. The course will comprise six sessions delivered as guided walkthroughs. Together these will provide the necessary programming skills for producing full longitudinal analyses reproducibly from beginning to middle and end (exploring raw data, performing analyses, and presenting results in publication-ready tables and figures).
The course will be built around a single real-world analysis to demonstrate the full quantitative research pipeline in R. Attendees will leave with an appreciation of the power of R, including how R can be used to perform many analyses in an efficient way (e.g., performing Outcome-Wide and Specification Curve Analyses or other ‘many-model’ approaches).
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