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Which schools and pupils respond to educational achievement surveys?: a focus on the English Programme for International Student Assessment sample
Author(s) -
Durrant Gabriele B.,
Schnepf Sylke V.
Publication year - 2018
Publication title -
journal of the royal statistical society: series a (statistics in society)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.103
H-Index - 84
eISSN - 1467-985X
pISSN - 0964-1998
DOI - 10.1111/rssa.12337
Subject(s) - pupil , sample (material) , mathematics education , psychology , scale (ratio) , multilevel model , academic achievement , pedagogy , geography , chemistry , cartography , neuroscience , machine learning , computer science , chromatography
Summary Non‐response is a major problem facing research in the social sciences including in education surveys. Hence, research is needed to understand non‐response patterns better as well as non‐response as a social phenomenon. Findings may contribute to improvements in the future designs of such surveys. Using logistic and multilevel logistic modelling we examine correlates of non‐response at the school and pupil level in the important educational achievement survey the ‘Programme for International Student Assessment’ (PISA) for England. The analysis exploits unusually rich auxiliary information on all schools and pupils sampled for PISA, whether responding or not, from two large‐scale administrative sources on pupils’ socio‐economic background and results in national public examinations. This information correlates highly with the PISA target variable. Findings show that characteristics that are associated with non‐response differ between the school and pupil levels. Our results also indicate that schools matter in explaining pupil level response, which is often ignored in non‐response analysis. Our findings have important implications for future education surveys. For example, if replacement schools are used to improve response, our results suggest that it may be more important to match initial and replacement schools on the socio‐economic composition of their pupils than on any of the factors that are currently used.