Conditioning: How background variables can influence PISA scores

Laura Zieger (), John Jerrim (), Jake Anders and Nikki Shure
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Laura Zieger: Department of Social Science, UCL Institute of Education, University College London
John Jerrim: Department of Social Science, UCL Institute of Education, University College London

No 20-09, CEPEO Working Paper Series from UCL Centre for Education Policy and Equalising Opportunities

Abstract: The Programme for International Student Assessment (PISA) is an international large-scale assessment which examines the educational achievement of 15-year-old students across the world. It has long become one of the key studies for evidence-based education policymaking across the globe. As result, PISA results and the methodology that they are based on should be robust, open and transparent. Yet, PISA receives significant criticism for its scaling model and the opaqueness in communicating it. One particular point of concern is the so-called "conditioning model", where background variables are used in the derivation of student achievement scores. The aim of this paper is to investigate this part of the scaling model and the impact it has upon the final scores. This includes varying the background variables of the conditioning model systematically and analysing the impact that this has on multiple measures. Our key finding is that the exact specification of the conditioning model matters and has substantial impact on average scores in some of the minor PISA domains (namely reading). It also has a major impact upon cross-national comparisons of educational inequality.

Keywords: PISA; conditioning model; research transparency; scaling model; cross-national comparison; educational performance; educational inequality; test scores (search for similar items in EconPapers)
JEL-codes: C10 C18 C55 I20 I21 I24 (search for similar items in EconPapers)
Pages: 63 pages
Date: 2020-04, Revised 2020-04
New Economics Papers: this item is included in nep-edu and nep-ure
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