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Revisiting fixed- and random-effects models: some considerations for policy-relevant education research

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  • Paul Clarke
  • Claire Crawford
  • Fiona Steele
  • Anna Vignoles
Abstract
The use of fixed (FE) and random effects (RE) in two-level hierarchical linear regression is discussed in the context of education research. We compare the robustness of FE models with the modelling flexibility and potential efficiency of those from RE models. We argue that the two should be seen as complementary approaches. We then compare both modelling approaches in our empirical examples. Results suggest a negative effect of special educational needs (SEN) status on educational attainment, with selection into SEN status largely driven by pupil level rather than school-level factors.

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  • Paul Clarke & Claire Crawford & Fiona Steele & Anna Vignoles, 2015. "Revisiting fixed- and random-effects models: some considerations for policy-relevant education research," Education Economics, Taylor & Francis Journals, vol. 23(3), pages 259-277, June.
    • Handle: RePEc:taf:edecon:v:23:y:2015:i:3:p:259-277
    DOI: 10.1080/09645292.2013.855705
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    1. Robert MacCoun, 2003. "Experimental and Quasi-Experimental Designs for Generalized Causal Inference , by William R. Shadish, Thomas D. Cook, and Donald T. Campbell. Boston: Houghton Mifflin, 2001, 623 pp., $65.56," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 22(2), pages 330-332.
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    3. Eric A. Hanushek & John F. Kain & Steven G. Rivkin, 2002. "Inferring Program Effects for Special Populations: Does Special Education Raise Achievement for Students with Disabilities?," The Review of Economics and Statistics, MIT Press, vol. 84(4), pages 584-599, November.
    4. Mundlak, Yair, 1978. "On the Pooling of Time Series and Cross Section Data," Econometrica, Econometric Society, vol. 46(1), pages 69-85, January.
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