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Peer effects and measurement error: The impact of sampling variation in school survey data (evidence from PISA)

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  • Micklewright, John
  • Schnepf, Sylke V.
  • Silva, Pedro N.

Abstract

Investigation of peer effects on achievement with sample survey data on schools may mean that only a random sample of the population of peers is observed for each individual. This generates measurement error in peer variables similar in form to the textbook case of errors-in-variables, resulting in the estimated peer group effects in an OLS regression model being biased towards zero. 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. We also use a Monte Carlo experiment to show how the extent of the attenuation bias rises as peer sample size falls. On average, the estimated peer effect is biased downwards by about one third when drawing a sample of peers of the size implied by the PISA survey design.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:ecoedu:v:31:y:2012:i:6:p:1136-1142
    DOI: 10.1016/j.econedurev.2012.07.015
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    Cited by:

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    3. Lidon Moliner & Francisco Alegre, 2020. "Effects of peer tutoring on middle school students’ mathematics self-concepts," PLOS ONE, Public Library of Science, vol. 15(4), pages 1-17, April.
    4. Ballatore, Rosario Maria & Paccagnella, Marco & Tonello, Marco, 2020. "Bullied because younger than my mates? The effect of age rank on victimisation at school," Labour Economics, Elsevier, vol. 62(C).
    5. Giannelli, Gianna Claudia & Rapallini, Chiara, 2016. "Immigrant student performance in Math: Does it matter where you come from?," Economics of Education Review, Elsevier, vol. 52(C), pages 291-304.
    6. 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.
    7. Pedro N. Silva & John Micklewright & Sylke V. Schnepf, 2012. "The impact of sampling variation on peer measures: a comment on a proposal to adjust estimates for measurement error," DoQSS Working Papers 12-12, Quantitative Social Science - UCL Social Research Institute, University College London.
    8. Lopez-Agudo, Luis Alejandro & González-Betancor, Sara M. & Marcenaro-Gutierrez, Oscar David, 2021. "Language at home and academic performance: The case of Spain," Economic Analysis and Policy, Elsevier, vol. 69(C), pages 16-33.
    9. Ana Balsa & Carlos Díaz, 2018. "Social interactions in health behaviors and conditions," Documentos de Trabajo/Working Papers 1802, Facultad de Ciencias Empresariales y Economia. Universidad de Montevideo..
    10. Bernhard Christopher Dannemann, 2020. "Better Off On Their Own? How Peer Effects Determine International Patterns of the Mathematics Gender Achievement Gap," Working Papers V-433-20, University of Oldenburg, Department of Economics, revised Nov 2020.
    11. Ryan Yeung & Phuong Nguyen-Hoang, 2016. "Endogenous peer effects: Fact or fiction?," The Journal of Educational Research, Taylor & Francis Journals, vol. 109(1), pages 37-49, January.
    12. Schnepf, Sylke, 2018. "Insights into survey errors of large scale educational achievement surveys," Working Papers 2018-05, Joint Research Centre, European Commission.
    13. Vardardottir, Arna, 2015. "The impact of classroom peers in a streaming system," Economics of Education Review, Elsevier, vol. 49(C), pages 110-128.

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    More about this item

    Keywords

    Peer effects; Measurement error; School surveys; Sampling variation; PISA;
    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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