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Non-response biases in surveys of schoolchildren: the case of the English Programme for International Student Assessment (PISA) samples

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  • John Micklewright
  • Sylke V. Schnepf
  • Chris Skinner

Abstract

We analyse response patterns to an important survey of schoolchildren, exploiting rich auxiliary information on respondents' and non-respondents' cognitive ability that is correlated both with response and the learning achievement that the survey aims to measure. The survey is the Programme for International Student Assessment (PISA), which sets response thresholds in an attempt to control the quality of data. We analyse the case of England for 2000, when response rates were deemed sufficiently high by the organizers of the survey to publish the results, and 2003, when response rates were a little lower and deemed of sufficient concern for the results not to be published. We construct weights that account for the pattern of non-response by using two methods: propensity scores and the generalized regression estimator. There is clear evidence of biases, but there is no indication that the slightly higher response rates in 2000 were associated with higher quality data. This underlines the danger of using response rate thresholds as a guide to quality of data.
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Suggested Citation

  • John Micklewright & Sylke V. Schnepf & Chris Skinner, 2012. "Non-response biases in surveys of schoolchildren: the case of the English Programme for International Student Assessment (PISA) samples," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 175(4), pages 915-938, October.
    • Handle: RePEc:bla:jorssa:v:175:y:2012:i:4:p:915-938
    DOI: j.1467-985X.2012.01036.x
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    Cited by:

    1. Rutherford, Matthew W. & O’Boyle, Ernest H. & Miao, Chao & Goering, Daniel & Coombs, Joseph E., 2017. "Do response rates matter in entrepreneurship research?," Journal of Business Venturing Insights, Elsevier, vol. 8(C), pages 93-98.
    2. Martin Grančay, 2020. "COVID-19 and Central European Tourism: The Competitiveness of Slovak Tourist Guides," Central European Business Review, Prague University of Economics and Business, vol. 2020(5), pages 81-98.
    3. Gabriele B. Durrant & Sylke V. Schnepf, 2018. "Which schools and pupils respond to educational achievement surveys?: a focus on the English Programme for International Student Assessment sample," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 181(4), pages 1057-1075, October.
    4. Jakubowski, Maciej & Pokropek, Artur, 2015. "Reading achievement progress across countries," International Journal of Educational Development, Elsevier, vol. 45(C), pages 77-88.
    5. Lisa Meehan & Gail Pacheco & Thomas Schober, 2023. "Basic Reading and Mathematics Skills and the Labour Market Outcomes of Young People: Evidence from PISA and Linked Administrative Data," The Economic Record, The Economic Society of Australia, vol. 99(327), pages 473-491, December.
    6. Laura Zieger & John Jerrim & Jake Anders & Nikki Shure, 2020. "Conditioning: How background variables can influence PISA scores," CEPEO Working Paper Series 20-09, UCL Centre for Education Policy and Equalising Opportunities, revised Apr 2020.
    7. Brick J. Michael, 2013. "Unit Nonresponse and Weighting Adjustments: A Critical Review," Journal of Official Statistics, Sciendo, vol. 29(3), pages 329-353, June.
    8. John Jerrim & Anna Vignoles & Ross Finnie, 2012. "University access for disadvantaged children: A comparison across English speaking countries," DoQSS Working Papers 12-11, Quantitative Social Science - UCL Social Research Institute, University College London.
    9. Daniel K. Lew & Amber Himes-Cornell & Jean Lee, 2015. "Weighting and Imputation for Missing Data in a Cost and Earnings Fishery Survey," Marine Resource Economics, University of Chicago Press, vol. 30(2), pages 219-230.
    10. Dan Hedlin, 2020. "Is there a 'safe area' where the nonresponse rate has only a modest effect on bias despite non‐ignorable nonresponse?," International Statistical Review, International Statistical Institute, vol. 88(3), pages 642-657, December.
    11. 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.

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    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General

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