BSTRACT
Children ‘in care’ have, on average, lower educational attainment than their peers. This article tests the hypothesis that many of these children can ‘catch-up’, if in stable placements and secondary schools ‘apparently effective’ with other children with ‘similar’ difficulties. In a cohort of 542,998 16-year-old English children in mainstream schools, those in care for at least a year were on average 148,465 ranks behind their peers on measured attainment at age 7. At age 16, 21% of this group had ‘caught up’ improving their ranking by at least this amount. Allowing for covariates, we found that schools were differentially effective for disadvantaged pupil groups defined by eligibility for free school meals at age 7, in the bottom 3 deciles of attainment at entry to secondary school, or deemed ‘in need’ or as having behavioural, emotional or social difficulties. As predicted, the conditions for children in care catching up related to placement stability and measures of their school’s apparent impact on these disadvantaged groups. In the ‘worst’ conditions 4% caught up as against 52% in the ‘best’. The results support the hypotheses that best practice can reduce the educational gaps between children in care, other low attaining groups and their peers.
Acknowledgments
We thank Professors Michael Rutter and Michael Yudkin for constructive comments on earlier drafts.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Notes
1. At the time of data collection, a score of 17 or above for Total Difficulties was categorised as ‘abnormal’. This range of scoring has since been divided and renamed into ‘high’ and ‘very high’ categories, but the clinical implications remain the same.
2. The analysis that produced these measures is sensitive to the model specification. It seemed possible that some factor – for example, coding mistakes in certain schools or the combination of regression to the mean and the clustering of children of low KS2 attainment in others – might inflate our estimates of the school level variance associated with the risk groups. We ran a model using a normalised outcome, Level 1 variance terms, and KS2 scores treated as a categorical variable divided into ten deciles (to allow for a possible non-linear relationship to outcome). This model reduced the variance attributable to the risk groups which nevertheless remained substantial. Graphical analysis also suggested that the relationship between the school measures and progress was similar over the range of KS2 attainment.
3. The deviance scores were 28,924 for the SILA model and 28,926 for the model.
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Notes on contributors
Ian Sinclair
Ian Sinclair is Professor Emeritus at the University of York and Consultant to the Rees Centre. His research interests focus on the evaluation of social services, particularly those provided for children in public care.
John Fletcher
John Fletcher was Statistical Consultant to the project. He organised the data, undertook the initial multi-level analyses and gave methodological advice on other analyses. Apart from a short spell in AI research, he has spent most of his career in government statistics, using state-of-the art methods to deal with survey non-response between the 1980s and the 2000s.
Aoife O’Higgins
Aoife O’Higgins is the Director of Research at What Works for Children’s Social Care. She leads the centre’s research portfolio, focusing primarily on evaluations of new and existing interventions.
Nikki Luke
Nikki Luke is a Research Fellow at the Rees Centre, University of Oxford. Her research interests include the educational experiences of children in care, and their mental health and well-being across the home and school contexts.
Sally Thomas
Sally Thomas is Professor of Education at the University of Bristol. Her research interests and publications over 30 years include educational quality, effectiveness and improvement, value added methodology, professional learning communities, citizenship, and education in developing countries including East Asia, Africa and South America.