IDEAS home Printed from https://ideas.repec.org/p/qss/dqsswp/1013.html
   My bibliography  Save this paper

Peer effects and measurement error: the impact of sampling variation in school survey data

Author

Listed:
  • John Micklewright

    (Depatment of Quantitative Social Science - Institute of Education, University of London.)

  • Sylke V. Schnepf

    (School of Social Sciences, University of Southampton, UK.)

  • Pedro N. Silva

    (Instituto Brasileiro de Geografia e Estatistica and Southampton Statistical Sciences Research Institute, University of Southampton.)

Abstract

Investigation of peer effects on achievement with sample survey data on schools may mean that only a random sample of peers is observed for each individual. This generates classical measurement error in peer variables, resulting in the estimated peer group effects in a regression model being biased towards zero under OLS model fitting. We investigate the problem using survey data for England from the Programme for International Student Assessment (PISA) linked to administrative microdata recording information for each PISA sample member's entire year cohort. We calculate a peer group measure based on these complete data and compare its use with a variable based on peers in just the PISA sample. The estimated attenuation bias in peer effect estimates based on the PISA data alone is substantial.

Suggested Citation

  • John Micklewright & Sylke V. Schnepf & Pedro N. Silva, 2010. "Peer effects and measurement error: the impact of sampling variation in school survey data," DoQSS Working Papers 10-13, Quantitative Social Science - UCL Social Research Institute, University College London.
  • Handle: RePEc:qss:dqsswp:1013
    as

    Download full text from publisher

    File URL: https://repec.ucl.ac.uk/REPEc/pdf/qsswp1013.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Gabriela Schütz & Heinrich W. Ursprung & Ludger Wößmann, 2008. "Education Policy and Equality of Opportunity," Kyklos, Wiley Blackwell, vol. 61(2), pages 279-308, May.
    2. Nicole Schneeweis & Rudolf Winter-Ebmer, 2008. "Peer effects in Austrian schools," Studies in Empirical Economics, in: Christian Dustmann & Bernd Fitzenberger & Stephen Machin (ed.), The Economics of Education and Training, pages 133-155, Springer.
    3. Geoffrey Woodhouse & Min Yang & Harvey Goldstein & Jon Rasbash, 1996. "Adjusting for Measurement Error in Multilevel Analysis," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 159(2), pages 201-212, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Duncan McVicar, 2012. "Cross Country Estimates of Peer Effects in Adolescent Smoking Using IV and School Fixed Effects," Melbourne Institute Working Paper Series wp2012n07, Melbourne Institute of Applied Economic and Social Research, The University of Melbourne.
    2. Jerrim, John & Lopez-Agudo, Luis Alejandro & Marcenaro-Gutierrez, Oscar D. & Shure, Nikki, 2017. "What happens when econometrics and psychometrics collide? An example using the PISA data," Economics of Education Review, Elsevier, vol. 61(C), pages 51-58.
    3. Duncan McVicar & Arnold Polanski, 2014. "Peer Effects in UK Adolescent Substance Use: Never Mind the Classmates?," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 76(4), pages 589-604, August.

    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. Nicole Schneeweis, 2011. "Educational institutions and the integration of migrants," Journal of Population Economics, Springer;European Society for Population Economics, vol. 24(4), pages 1281-1308, October.
    2. Elke Lüdemann, 2011. "Schooling and the Formation of Cognitive and Non-cognitive Outcomes," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 39, April.
    3. Marchionni, Mariana & Pinto, Florencia & Vazquez, Emmanuel, 2013. "Determinantes de la desigualdad en el desempeño educativo en la Argentina [Determinants of the inequality in PISA test scores in Argentina]," MPRA Paper 56421, University Library of Munich, Germany.
    4. Marchionni, Mariana & Vazquez, Emmanuel & Pinto, Florencia, 2012. "Desigualdad educativa en la Argentina. Análisis en base a los datos PISA 2009 [Education Inequality in Argentina. An analysis based on PISA 2009 data]," MPRA Paper 56420, University Library of Munich, Germany.
    5. Micklewright, John & Schnepf, Sylke V. & Silva, Pedro N., 2012. "Peer effects and measurement error: The impact of sampling variation in school survey data (evidence from PISA)," Economics of Education Review, Elsevier, vol. 31(6), pages 1136-1142.
    6. Piopiunik, Marc, 2014. "The effects of early tracking on student performance: Evidence from a school reform in Bavaria," Economics of Education Review, Elsevier, vol. 42(C), pages 12-33.
    7. Sven Resnjanskij & Jens Ruhose & Simon Wiederhold & Ludger Wößmann, 2021. "Mentoring Improves the Labor-Market Prospects of Highly Disadvantaged Adolescents," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 74(02), pages 31-38, February.
    8. Zlata Bruckauf & UNICEF Innocenti Research Centre, 2016. "Falling Behind: Socio-demographic profiles of educationally disadvantaged youth. Evidence from PISA 2000-2012," Papers inwopa837, Innocenti Working Papers.
    9. Paul Anand & Jere R. Behrman & Hai-Anh H. Dang & Sam Jones, 2018. "Inequality of opportunity in education: Accounting for the contributions of Sibs, schools and sorting across East Africa," Working Papers 480, ECINEQ, Society for the Study of Economic Inequality.
    10. Mahmut Ozer & Matjaž Perc, 2020. "Dreams and realities of school tracking and vocational education," Palgrave Communications, Palgrave Macmillan, vol. 6(1), pages 1-7, December.
    11. van Elk, Roel & van der Steeg, Marc & Webbink, Dinand, 2011. "Does the timing of tracking affect higher education completion?," Economics of Education Review, Elsevier, vol. 30(5), pages 1009-1021, October.
    12. Missinne, Sarah & Colman, Elien & Bracke, Piet, 2013. "Spousal influence on mammography screening: A life course perspective," Social Science & Medicine, Elsevier, vol. 98(C), pages 63-70.
    13. Magali Jaoul-Grammare & Brice Magdalou, 2013. "Opportunities in Higher Education: An Application to France," Annals of Economics and Statistics, GENES, issue 111-112, pages 295-325.
    14. Canaan, Serena & Mouganie, Pierre & Zhang, Peng, 2022. "The Long-Run Educational Benefits of High-Achieving Classrooms," IZA Discussion Papers 15039, Institute of Labor Economics (IZA).
    15. Lefranc, Arnaud & Pistolesi, Nicolas & Trannoy, Alain, 2009. "Equality of opportunity and luck: Definitions and testable conditions, with an application to income in France," Journal of Public Economics, Elsevier, vol. 93(11-12), pages 1189-1207, December.
    16. Marrero,Gustavo Alberto & Rodríguez,Juan Gabriel & Van Der Weide,Roy, 2021. "Does Race and Gender Inequality Impact Income Growth ?," Policy Research Working Paper Series 9865, The World Bank.
    17. Égert, Balázs & Botev, Jarmila & Turner, David, 2020. "The contribution of human capital and its policies to per capita income in Europe and the OECD," European Economic Review, Elsevier, vol. 129(C).
    18. Gabriela Schütz & Heinrich W. Ursprung & Ludger Wößmann, 2008. "Education Policy and Equality of Opportunity," Kyklos, Wiley Blackwell, vol. 61(2), pages 279-308, May.
    19. Murat Marina, 2012. "Do Immigrant Students Succeed? Evidence from Italy and France," Global Economy Journal, De Gruyter, vol. 12(3), pages 1-22, September.
    20. Guido Neidhöfer, 2019. "Intergenerational mobility and the rise and fall of inequality: Lessons from Latin America," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 17(4), pages 499-520, December.

    More about this item

    Keywords

    peer effects; measurement error; school surveys; sampling variation;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • I21 - Health, Education, and Welfare - - Education - - - Analysis of 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:qss:dqsswp:1013. 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: Dr Neus Bover Fonts (email available below). General contact details of provider: https://edirc.repec.org/data/dqioeuk.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.