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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
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    File URL: https://repec.ucl.ac.uk/REPEc/pdf/qsswp1013.pdf
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    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.
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    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.

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    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

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