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What Happens When Econometrics and Psychometrics Collide? An Example Using the PISA Data

Listed author(s):
  • Jerrim, John

    ()

    (University College London)

  • Lopez-Agudo, Luis Alejandro

    ()

    (University of Malaga)

  • Marcenaro-Gutierrez, Oscar D.

    ()

    (University of Malaga)

  • Shure, Dominique

    ()

    (University College London)

International large-scale assessments such as PISA are increasingly being used to benchmark the academic performance of young people across the world. Yet many of the technicalities underpinning these datasets are misunderstood by applied researchers, who sometimes fail to take their complex sample and test designs into account. The aim of this paper is to generate a better understanding amongst economists about how such databases are created, and what this implies for the empirical methodologies one should (or should not) apply. We explain how some of the modelling strategies preferred by economists seem to be at odds with the complex test design, and provide clear advice on the types of robustness tests that are therefore needed when analyzing these datasets. In doing so, we hope to generate a better understanding of international large-scale education databases, and promote better practice in their use.

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File URL: http://ftp.iza.org/dp10847.pdf
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Paper provided by Institute for the Study of Labor (IZA) in its series IZA Discussion Papers with number 10847.

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Length: 36 pages
Date of creation: Jun 2017
Handle: RePEc:iza:izadps:dp10847
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  1. David Kiss, 2013. "Are immigrants and girls graded worse? Results of a matching approach," Education Economics, Taylor & Francis Journals, vol. 21(5), pages 447-463, December.
  2. Asako Ohinata & Jan C. van Ours, 2013. "How Immigrant Children Affect the Academic Achievement of Native Dutch Children," Economic Journal, Royal Economic Society, vol. 0, pages 308-331, 08.
  3. Eric A. Hanushek & Ludger Wössmann, 2006. "Does Educational Tracking Affect Performance and Inequality? Differences- in-Differences Evidence Across Countries," Economic Journal, Royal Economic Society, vol. 116(510), pages 63-76, 03.
  4. S. Mahuteau & K. Mavromaras, 2014. "An analysis of the impact of socio-economic disadvantage and school quality on the probability of school dropout," Education Economics, Taylor & Francis Journals, vol. 22(4), pages 389-411, August.
  5. Herrero, Carmen & Mendez, Ildefonso & Villar, Antonio, 2014. "Analysis of group performance with categorical data when agents are heterogeneous: The evaluation of scholastic performance in the OECD through PISA," Economics of Education Review, Elsevier, vol. 40(C), pages 140-151.
  6. Lounkaew, Kiatanantha, 2013. "Explaining urban–rural differences in educational achievement in Thailand: Evidence from PISA literacy data," Economics of Education Review, Elsevier, vol. 37(C), pages 213-225.
  7. Ohinata, Asako & van Ours, Jan C., 2011. "How Immigrant Children Affect the Academic Achievement of Native Dutch Children," IZA Discussion Papers 6212, Institute for the Study of Labor (IZA).
  8. Jensen, Peter & Rasmussen, Astrid Würtz, 2011. "The effect of immigrant concentration in schools on native and immigrant children's reading and math skills," Economics of Education Review, Elsevier, vol. 30(6), pages 1503-1515.
  9. 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.
  10. Jan van Ours, 2008. "When do children read books?," Education Economics, Taylor & Francis Journals, vol. 16(4), pages 313-328.
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