Non-response biases in surveys of school children: the case of the English PISA samples

John Micklewright (), Sylke Schnepf and Chris Chris Skinner ()
Additional contact information
Chris Chris Skinner: School of Social Sciences, University of Southampton, UK.

No 10-04, DoQSS Working Papers from Quantitative Social Science - UCL Social Research Institute, University College London

Abstract: We analyse response patterns to an important survey of school children, 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 data quality. We analyse the case of England for 2000 when response rates were deemed high enough by the PISA organisers 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publish the results, and 2003, when response rates were a little lower and deemed of sufficient concern for the results not to be published.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 using two methods, propensity scores and the GREG 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 data quality.

Keywords: Non-response; bias; school survey; data linkage; PISA (search for similar items in EconPapers)
JEL-codes: C83 I21 (search for similar items in EconPapers)
Pages: 37 pages
Date: 2010
New Economics Papers: this item is included in nep-edu and nep-ure
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://repec.ucl.ac.uk/REPEc/pdf/qsswp1004.pdf (application/pdf)

Related works:
Working Paper: Non-Response Biases in Surveys of School Children: The Case of the English PISA Samples (2010)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:qss:dqsswp:1004

Access Statistics for this paper

More papers in DoQSS Working Papers from Quantitative Social Science - UCL Social Research Institute, University College London Quantitative Social Science, Social Research Institute, 55-59 Gordon Square, London WC1H 0NU. Contact information at EDIRC.
Bibliographic data for series maintained by Dr Neus Bover Fonts ().