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I am a Professor of Psychology focusing on children and young people’s mental health and special educational needs. In my research group, we love fitting advanced statistical models to existing large and complex datasets (eg, longitudinal cohort studies or administrative data).
I am interested in hearing from potential PhD students who are interested in secondary data analysis, are happy to undertake further training in advanced quantitative methods, and would like to develop projects on the following topics:
I lead the Child and Adolescent Neurodevelopmental Diversity (CANDY) Group; we focus on special educational needs and mental health during childhood and adolescence.
We love quantitative data-in fact we love big datasets! A key strength of our group is longitudinal modelling of large epidemiological and administrative datasets. We use big data to study:
Analysis Keywords: Longitudinal Modelling, Latent Class Analysis, Growth Curve Modelling, Mixture Modelling, Structural Equation Modelling, Factor Analysis, Mediation Analysis, Latent Class Growth Modelling, Multilevel Modelling, Measurement Invariance, Twin Modelling, Polygenic Scoring, Genome-Wide Association Study, Network Analysis, Propensity Score Matching.
Datasets: Avon Longitudinal Study of Parents and Children, Millennium Cohort Study, Manchester Language Study, Twins Early Development Study, British Cohort Study, Neuroscience in Psychiatry Network, Born in Bradford Study, National Pupil Database, Police National Computer.
The current system for identifying and supporting children and young people with special educational needs and mental health difficulties is not fit for purpose. In this project, we are using existing data from four large population-based studies (ALSPAC, BCS, MCS, TEDS) to understand how to better identify and support children and young people with special educational needs and mental health difficulties in schools. We are trying to find alternatives to diagnostic labels (eg, depression, anxiety, ADHD, autism) which often don’t capture the needs of children and young people who struggle at school. We have received £332,000 from the Nuffield Foundation for this work.
Young people with special educational needs and mental health difficulties are more likely to be cautioned or charged for a violent offence compared to those without such difficulties. In this project, we are using existing data from the National Pupil Database, National Police Computer, and the ALSPAC dataset to understand how to prevent young people with special educational needs and mental health difficulties from entering the criminal justice system. We are trying to identify levers for change during school (eg, EHCPs, early identification, school type etc.). We have received £160,000 from the Youth Endowment Fund for this work.
The evidence base for outdoor adventure learning interventions (eg, gorge walking, camping, hiking) is limited. It is unclear which elements of adventure learning programmes impact upon children and young people’s social and emotional functioning, and educational outcomes. In this project, we are evaluating two adventure learning interventions concurrently in a three-armed randomised controlled trial in around 100 schools. We have received £398,283 from the Education Endowment Foundation for this work.

https://linktr.ee/umartoseeb