Our research


We undertake multidisciplinary research on issues that affect all our lives: child development, education, social mobility, health and wellbeing, families and family life, and ageing. We also conduct research into survey methods, and applied statistical methods.

Our applied statistical methods programme specialises in methods for dealing with attrition, causal identification, and data harmonisation.

Our research helps tackle some of the key challenges we face in our society today.

To find out more, explore the links below.

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Does the language of 11-year-olds predict their future?

For this project the research team used machine learning tools to explore whether essays written by 11-year-olds in 1969 provided clues to their economic status, physical activity, health, and cognitive function in later life.


Childhood mental health trajectories and lifetime consequences: a cross-cohort programme of work

Drawing on data from all four of our cohort studies, this project examined young people’s mental health trajectories today in the context of previous generations. The project was part of the Cross Cohort Research Programme.


Parental wealth in childhood and its relationship with children’s development and predictors of wealth in adult life

Using data from three of our cohort studies, this project aimed to understand how parents’ long-term financial position shapes their children’s outcomes from an early stage. This was part of the Cross Cohort Research Programme.


Social mobility mechanisms and consequences

This project provided empirical evidence on curricula delivery variation in the UK and institutional differences through analysis of Next Steps and linked National Pupil Database data. This was part of our Cross Cohort Research Programme.


Cross Cohort Research Programme

Incorporating seven projects, this programme explored two key themes which are both central to government policy: healthy lifestyles and the transmission of advantage and disadvantage from one generation to the next.


What works for wellbeing cross-cutting capabilities evidence programme

Research using longitudinal data is able to provide invaluable insights into how as a society we can work together to improve our quality of life. This research project included an analysis of how wellbeing and mental health have changed across generations and…


In-work poverty and retirement attitudes among a cohort born in 1958

The aim of this project was to explore retirement-related attitudes, aspirations, expectations, and plans for retirement among adults in their mid to late 50s.


Initial findings from the Millennium Cohort Study Age 11 Sweep

The fifth MCS survey took place during 2012 when participants were aged 11. Our initial findings from the age 11 survey cover a range of themes, from family structure to child cognitive development.


Pathways from environmental risk to children’s psychological maladjustment and resilience

This project tested how neighbourhood, family poverty and other adverse circumstances are related to children’s wellbeing, as gauged through emotional and behavioural outcomes.


Life history and healthy ageing

This project was part of a collaborative research programme entitled ‘Healthy Ageing across the Life Course’ (HALCyon). This programme was funded under the New Dynamics of Ageing initiative – a cross council multi-disciplinary research.


Parenting and contact before and after separation

This project analysed data from the first four surveys of the Millennium Cohort Study, at ages 9 months, 3 years, 5 years, 7 years and 11 years. It looked specifically at factors related to parents’ contact with their children after separation,…


Trajectories and transitions in the cognitive and educational development of disabled children and young people

The aim of the study was to enhance our understanding of disabled children’s early cognitive development and their subsequent educational transitions.

Contact us

Centre for Longitudinal Studies
UCL Social Research Institute

20 Bedford Way
London WC1H 0AL

Email: clsfeedback@ucl.ac.uk