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The Choice between fixed and random effects models: some considerations for educational research


  • Paul Clarke
  • Claire Crawford
  • Fiona Steele
  • Anna Vignoles



We discuss the use of fixed and random effects models in the context of educational research and set out the assumptions behind the two modelling approaches. To illustrate the issues that should be considered when choosing between these approaches, we analyse the determinants of pupil achievement in primary school, using data from the Avon Longitudinal Study of Parents and Children. We conclude that a fixed effects approach will be preferable in scenarios where the primary interest is in policy-relevant inference about the effects of individual characteristics, but the process through which pupils are selected into schools is poorly understood or the data are too limited to adjust for the effects of selection. In this context, the robustness of the fixed effects approach to the random effects assumption is attractive, and educational researchers should consider using it, even if only to assess the robustness of estimates obtained from random effects models. On the other hand, when the selection mechanism is fairly well understood and the researcher has access to rich data, the random effects model should naturally be preferred because it can produce policy-relevant estimates while allowing a wider range of research questions to be addressed. Moreover, random effects estimators of regression coefficients and shrinkage estimators of school effects are more statistically efficient than those for fixed effects.

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  • Paul Clarke & Claire Crawford & Fiona Steele & Anna Vignoles, 2010. "The Choice between fixed and random effects models: some considerations for educational research," The Centre for Market and Public Organisation 10/240, Department of Economics, University of Bristol, UK.
  • Handle: RePEc:bri:cmpowp:10/240

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    Cited by:

    1. Claire Crawford & Lindsey Macmillan & Anna Vignoles, 2015. "When and why do initially high attaining poor children fall behind?," DoQSS Working Papers 15-08, Department of Quantitative Social Science - UCL Institute of Education, University College London.
    2. Ferguson, Neil T.N. & Michaelsen, Maren M., 2015. "Money changes everything? Education and regional deprivation revisited," Economics of Education Review, Elsevier, vol. 48(C), pages 129-147.
    3. Babar Shahbaz & Abid Suleri & Mohsin Ali & Huma Khan & Georgina Sturge & Richard Mallett & Jessica Hagen-Zanker, 2017. "Tracking Change Islamabad in Livelihoods, Service Delivery and Governance: Evidence from a 2012-2015 Panel Survey in Pakistan," Working Papers id:11953, eSocialSciences.
    4. Gitto, Lara & Minervini, Leo Fulvio & Monaco, Luisa, 2012. "University dropouts: supply-side issues in Italy," MPRA Paper 56656, University Library of Munich, Germany, revised Nov 2013.
    5. Gitto, Lara & Minervini, Leo Fulvio & Monaco, Luisa, 2016. "University dropouts in Italy: Are supply side characteristics part of the problem?," Economic Analysis and Policy, Elsevier, vol. 49(C), pages 108-116.
    6. Nikolaev, Boris, 2016. "Does other people's education make us less happy?," Economics of Education Review, Elsevier, vol. 52(C), pages 176-191.
    7. Małgorzata Mikucka & Ester Rizzi, 2016. "Does it take a village to raise a child?," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 34(34), pages 943-994, June.

    More about this item


    fixed effects; random effects; multilevel modelling; education; pupil achievement;

    JEL classification:

    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • I21 - Health, Education, and Welfare - - Education - - - Analysis of Education

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