IDEAS home Printed from https://ideas.repec.org/p/bri/cmpowp/09-208.html

The Limitations of Using School League Tables to Inform School Choice

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.bristol.ac.uk/cmpo/publications/papers/2009/wp208.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. 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.
    2. John F. Y. Brookfield, 2001. "Predicting the future," Nature, Nature, vol. 411(6841), pages 999-999, June.
    3. 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.
    4. 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.
    5. 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.
    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)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Anders Skrondal & Sophia Rabe‐Hesketh, 2009. "Prediction in multilevel generalized linear models," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 172(3), pages 659-687, June.
    3. Deborah Wilson & Anete Piebalga, 2008. "Accurate performance measure but meaningless ranking exercise? An analysis of the English school league tables," The Centre for Market and Public Organisation 07/176, The Centre for Market and Public Organisation, University of Bristol, UK.
    4. Pickery, Jan, 2002. "Contextual effects on the vote in Germany: A multilevel analysis," Discussion Papers, Research Unit: Institutions and Social Change FS III 02-202, WZB Berlin Social Science Center.
    5. Adnett, Nick & Bougheas, Spiros & Davies, Peter, 2002. "Market-based reforms of public schooling: some unpleasant dynamics," Economics of Education Review, Elsevier, vol. 21(4), pages 323-330, August.
    6. J Taylor, 2007. "The impact of the specialist schools programme on exam results," Working Papers 582526, Lancaster University Management School, Economics Department.
    7. Isabella Sulis & Mariano Porcu & Vincenza Capursi, 2019. "On the Use of Student Evaluation of Teaching: A Longitudinal Analysis Combining Measurement Issues and Implications of the Exercise," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 142(3), pages 1305-1331, April.
    8. Gwyn Bevan & Richard Hamblin, 2009. "Hitting and missing targets by ambulance services for emergency calls: effects of different systems of performance measurement within the UK," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 172(1), pages 161-190, January.
    9. George Leckie, 2022. "A celebration of Harvey Goldstein’s lifetime contributions: Memories of working with Harvey Goldstein on educational research and statistics," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(3), pages 758-762, July.
    10. Nicholas Tibor Longford, 2016. "Decision Theory Applied to Selecting the Winners, Ranking, and Classification," Journal of Educational and Behavioral Statistics, , vol. 41(4), pages 420-442, August.
    11. Sylvia Richardson, 2022. "Statistics in times of increasing uncertainty," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(4), pages 1471-1496, October.
    12. 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.
    13. Columbu, Silvia & Porcu, Mariano & Sulis, Isabella, 2021. "University choice and the attractiveness of the study area: Insights on the differences amongst degree programmes in Italy based on generalised mixed-effect models," Socio-Economic Planning Sciences, Elsevier, vol. 74(C).
    14. Heikki Pursiainen & Mika Kortelainen & Jenni Pääkkönen, 2014. "Impact of School Quality on Educational Attainment - Evidence from Finnish High Schools," ERSA conference papers ersa14p711, European Regional Science Association.
    15. R. Bellio & E. Gori, 2003. "Impact evaluation of job training programmes: Selection bias in multilevel models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 30(8), pages 893-907.
    16. repec:lan:wpaper:991 is not listed on IDEAS
    17. 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.
    18. Nick Adnett & Peter Davies, 2005. "Competition between or within schools? Re-assessing school choice," Education Economics, Taylor & Francis Journals, vol. 13(1), pages 109-121.
    19. Kathleen Carey, 2000. "A multilevel modelling approach to analysis of patient costs under managed care," Health Economics, John Wiley & Sons, Ltd., vol. 9(5), pages 435-446, July.
    20. 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.
    21. 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.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;

    JEL classification:

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

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bri:cmpowp:09/208. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/cmbriuk.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.