Phone: 020 7612 6661
Email: alice.sullivan@ucl.ac.uk

Alice’s research interests are focussed on social and educational inequalities and the intergenerational transmission of advantage and disadvantage.

Phone: 020 7911 5566
Email: morag.henderson@ucl.ac.uk

As Director of both MCS and Next Steps, Professor Henderson leads the strategic and scientific direction of the studies and is responsible for overseeing all data collections. She also leads the training programme at the Centre for Longitudinal Studies.

Professor Henderson’s main area of research is inequalities across the life course. More specifically, she examines intergenerational educational mobility, social isolation, mental health and wellbeing. She holds a DPhil in Sociology from the University of Oxford.

Phone: 020 7612 6288
Email: vanessa.moulton@ucl.ac.uk

Vanessa is a psychologist, with a strong interest in multidisciplinary social science. Her research interests include using longitudinal and secondary data analysis to examine the influence of the earlier life course on children’s and adult mental health, cognitive, educational and socio-economic outcomes. In addition, Vanessa co-coordinates the CLS cohort training workshops and webinars.

Phone: 020 7612 6107
Email: g.ploubidis@ucl.ac.uk

George is Professor of Population Health and Statistics at the UCL Social Research Institute and currently holds the posts of Principal Investigator of the National Child Development Study and 1970 British Cohort Study at the Centre for Longitudinal Studies. Prior to joining UCL he held posts at the London School of Hygiene and Tropical Medicine and the University of Cambridge. George is a multidisciplinary quantitative social scientist and a longitudinal population surveys methodologist. His main research interests relate to socioeconomic and demographic determinants of health over the life course and the mechanisms that underlie generational differences in health and mortality. His methodological work in longitudinal surveys focusses on applications for handling missing data, causal inference and measurement error.