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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publish the results, and 2003, when response rates were a little lower and deemed of sufficient concern for the results not to be published.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. Jakubowski, Maciej & Pokropek, Artur, 2015. "Reading achievement progress across countries," International Journal of Educational Development, Elsevier, vol. 45(C), pages 77-88.
    2. Brick J. Michael, 2013. "Unit Nonresponse and Weighting Adjustments: A Critical Review," Journal of Official Statistics, De Gruyter Open, vol. 29(3), pages 329-353, June.
    3. John Jerrim & Anna Vignoles & Ross Finnie, 2012. "University access for disadvantaged children: A comparison across English speaking countries," DoQSS Working Papers 12-11, Department of Quantitative Social Science - UCL Institute of Education, University College London.
    4. 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.
    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.

    More about this item

    JEL classification:

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

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