Peter Martin, Senior Research Fellow
Peter is Lecturer in Applied Statistics at the Department of Applied Health Research at University College London. As a statistician, he has been involved in the evaluation of child and adolescent mental health services, the analysis of Hospital Episode Statistics, as well as survey design and analysis.
His research has traversed a variety of areas, including life course studies; social attitudes, racism and prejudice; child mental health; and research methodology. He is interested in the social and family determinants of child mental health, health inequalities, as well as in research on treatments and services for children and families.
Please see his UCL page for more information.
Martin, P., Davies, R., Macdougall, A., Ritchie, B., Vostanis, P., Whale, A., & Wolpert, M. (2017). Developing a case mix classification for child and adolescent mental health services: the influence of presenting problems, complexity factors and service providers on number of appointments. Journal of Mental Health, 1-8. Read more
McMunn, A.; Martin, P.; Kelly, Y. & Sacker, A. (2015) Fathers' Involvement: Correlates and Consequences for Child Socioemotional Behavior in the United Kingdom. Journal of Family Issues. Read more
Wolpert, M.; Deighton, J.; De Francesco, D.; Martin, P.; Fonagy, P. & Ford, T. (2014) ‘From “reckless” to “mindful” in the use of outcome data to inform service-level performance management: perspectives from child mental health’. British Medical Journal: Quality and Safety 23 (4): 272-276.
For a full list of publications, click here.
Introduction to Research Methods
Introduction to Statistical Data Analysis with SPSS
Multivariate Data Analysis for Research
Current UCL PhD students
Sally O'Keeffe (2015-). Drop-out in adolescent psychotherapy
Antonella Cirasola (2017-). The role of Therapeutic Alliance in Psychological Therapies for adolescent depression
Tom Poulton (2017 - ). Socio-economic inequalities in outcomes of bowel surgery
Alistair Connell (2017 - ). The implementation of a digitally enabled care pathway for the recognition and management of acute kidney injury