Fiona Steele’s research interests are in developments of statistical methods that are motivated by social science problems. Her areas of expertise include longitudinal data analysis, multilevel modelling, survival analysis, and simultaneous equations modelling. She has worked on a range of applications in demography (e.g. residential mobility, union formation and dissolution, and contraceptive use dynamics), education (the consequences of parental divorce for children’s educational outcomes, the impact of school resources on pupil attainment), family psychology (reciprocal influences between parents and children, sibling interactions), and health (child health, mental health and employment transitions, determinants and consequences of stress among nurses).
She has directed several research grants funded by the Economic and Social Research Council (ESRC), including the LEMMA node of the National Centre for Research Methods and a project on the interrelationships between housing transitions and fertility in Britain and Australia. As part of the LEMMA project, she led the development of a popular online training course on multilevel modelling which currently has over 20,000 registered users worldwide. Her most recent project, funded jointly by ESRC and EPSRC, focuses on methods for the analysis of longitudinal dyadic data, with applications to a study of intergenerational exchanges of support using panel data from the UK Household Longitudinal Study.
Fiona first joined in LSE in 1996 as Lecturer in Statistics and Research Methodology. She then worked at the Institute of Education, University of London 2001-2005, followed by the University of Bristol 2005-2013 where she was Professor of Social Statistics and Director of the Centre for Multilevel Modelling. She returned to LSE in 2013.
Fiona was awarded the Royal Statistical Society Guy Medal in Bronze in 2008 and elected a Fellow of the British Academy in 2009. She was appointed an Officer of the Order of the British Empire (OBE) in 2011 for services to social sciences.