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The Limitations of Using School League Tables to Inform School Choice

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  • George Leckie
  • Harvey Goldstein

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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, Department of Economics, University of Bristol, UK.
  • Handle: RePEc:bri:cmpowp:09/208
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    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.
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    1. repec:oxf:wpaper:747 is not listed on IDEAS
    2. Isabella Sulis & Mariano Porcu, 2015. "Assessing Divergences in Mathematics and Reading Achievement in Italian Primary Schools: A Proposal of Adjusted Indicators of School Effectiveness," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 122(2), pages 607-634, June.
    3. Leonardo Grilli & Carla Rampichini, 2015. "Specification of random effects in multilevel models: a review," Quality & Quantity: International Journal of Methodology, Springer, vol. 49(3), pages 967-976, May.
    4. Tommaso Agasisti & Francesca Ieva & Anna Maria Paganoni, 2017. "Heterogeneity, school-effects and the North/South achievement gap in Italian secondary education: evidence from a three-level mixed model," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 26(1), pages 157-180, March.
    5. Allen, Rebecca & Burgess, Simon, 2013. "Evaluating the provision of school performance information for school choice," Economics of Education Review, Elsevier, vol. 34(C), pages 175-190.
    6. repec:bla:jorssa:v:180:y:2017:i:1:p:315-340 is not listed on IDEAS
    7. Steele, Fiona & Clarke, Paul & Kuha, Jouni, 2018. "Modeling within-household associations in household panel studies," LSE Research Online Documents on Economics 88162, London School of Economics and Political Science, LSE Library.
    8. Michele La Rocca & Maria Lucia Parrella & Ilaria Primerano & Isabella Sulis & Maria Prosperina Vitale, 2017. "An integrated strategy for the analysis of student evaluation of teaching: from descriptive measures to explanatory models," Quality & Quantity: International Journal of Methodology, Springer, vol. 51(2), pages 675-691, March.
    9. Maria Ferrão, 2012. "On the stability of value added indicators," Quality & Quantity: International Journal of Methodology, Springer, vol. 46(2), pages 627-637, February.
    10. Isabella Sulis & Mariano Porcu, 2012. "Comparing degree programs from students’ assessments: A LCRA-based adjusted composite indicator," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 21(2), pages 193-209, June.
    11. Gerdes, Christer, 2015. "Does Performance Information Affect Job Seekers in Selecting Private Providers in Voucher-Based ALMP Programs?," IZA Discussion Papers 8992, Institute for the Study of Labor (IZA).
    12. repec:spr:soinre:v:133:y:2017:i:3:d:10.1007_s11205-016-1407-1 is not listed on IDEAS
    13. Bruno ARPINO & Roberta VARRIALE, 2010. "Assessing The Quality Of Institutions’ Rankings Obtained Through Multilevel Linear Regression Models," Journal of Applied Economic Sciences, Spiru Haret University, Faculty of Financial Management and Accounting Craiova, vol. 5(1(11)_Spr), pages 7-22.
    14. Bruno Arpino & Roberta Varriale, 2009. "Assessing the quality of institutions' rankings obtained through multilevel linear regression models," Working Papers 019, "Carlo F. Dondena" Centre for Research on Social Dynamics (DONDENA), Università Commerciale Luigi Bocconi.
    15. Samantha Parsons & Lucinda Platt, 2014. "Disabled children's cognitive development in the early years," DoQSS Working Papers 14-15, Department of Quantitative Social Science - UCL Institute of Education, University College London.
    16. Luis Alejandro Lopez-Agudo & Oscar David Marcenaro Gutierrez, 2016. "Identifying effective teachers: The case study of Spain," Investigaciones de Economía de la Educación volume 11,in: José Manuel Cordero Ferrera & Rosa Simancas Rodríguez (ed.), Investigaciones de Economía de la Educación 11, edition 1, volume 11, chapter 18, pages 349-366 Asociación de Economía de la Educación.

    More about this item

    Keywords

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

    JEL classification:

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

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