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The Limitations of Using School League Tables to Inform School Choice

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  • George Leckie
  • Harvey Goldstein

Abstract

In England, so-called ‘league tables’ based upon examination results and test scores are published annually, ostensibly to inform parental choice of secondary schools. A crucial limitation of these tables is that the most recent published information is based on the current performance of a cohort of pupils who entered secondary schools several years earlier, whereas for choosing a school it is the future performance of the current cohort that is of interest. We show that there is substantial uncertainty in predicting such future performance and that incorporating this uncertainty leads to a situation where only a handful of schools’ future performances can be separated from both the overall mean and from one another with an acceptable degree of precision. This suggests that school league tables, including value-added ones, have very little to offer as guides to school choice.

Suggested Citation

  • George Leckie & Harvey Goldstein, 2009. "The Limitations of Using School League Tables to Inform School Choice," The Centre for Market and Public Organisation 09/208, The Centre for Market and Public Organisation, University of Bristol, UK.
  • Handle: RePEc:bri:cmpowp:09/208
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    File URL: http://www.bristol.ac.uk/cmpo/publications/papers/2009/wp208.pdf
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    References listed on IDEAS

    as
    1. David Afshartous & Michael Wolf, 2007. "Avoiding ‘data snooping’ in multilevel and mixed effects models," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 170(4), pages 1035-1059, October.
    2. Harvey Goldstein & Michael J. R. Healy, 1995. "The Graphical Presentation of a Collection of Means," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 158(1), pages 175-177, January.
    3. John F. Y. Brookfield, 2001. "Predicting the future," Nature, Nature, vol. 411(6841), pages 999-999, June.
    4. Harvey Goldstein & Sally Thomas, 1996. "Using Examination Results as Indicators of School and College Performance," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 159(1), pages 149-163, January.
    5. Sheila M. Bird & Cox Sir David & Vern T. Farewell & Goldstein Harvey & Holt Tim & Smith Peter C., 2005. "Performance indicators: good, bad, and ugly," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 168(1), pages 1-27, January.
    6. Stephen W. Raudenbush & JDouglas Willms, 1995. "The Estimation of School Effects," Journal of Educational and Behavioral Statistics, , vol. 20(4), pages 307-335, December.
    7. Harvey Goldstein & David J. Spiegelhalter, 1996. "League Tables and Their Limitations: Statistical Issues in Comparisons of Institutional Performance," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 159(3), pages 385-409, May.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Examination results; Institutional comparisons; League tables; Multilevel modelling; Performance indicators; Ranking; School choice; School effectiveness; Value-added;
    All these keywords.

    JEL classification:

    • I2 - Health, Education, and Welfare - - Education

    NEP fields

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