IDEAS home Printed from https://ideas.repec.org/p/qss/dqsswp/1704.html
   My bibliography  Save this paper

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

Author

Listed:
  • John Jerrim

    () (Department of Social Science, UCL Institute of Education, University College London)

  • Luis Alejandro Lopez-Agudo

    () (Departamento de Economía Aplicada (Estadística y Econometría). Facultad de Ciencias Económicas y Empresariales. Universidad de Málaga)

  • Oscar D. Marcenaro-Gutierrez

    () (Departamento de Economía Aplicada (Estadística y Econometría). Facultad de Ciencias Económicas y Empresariales. Universidad de Málaga)

  • Nikki Shure

    () (Department of Social Science, UCL Institute of Education and Institute of Labor Economics)

Abstract

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 miss-understood by applied researchers, who sometimes fail to take into account their complex survey and test designs. 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. In doing so, we explain how some of the modelling strategies preferred by economists is at odds with the design of these studies. In doing so, we hope to generate a better understanding of international large-scale education datasets, and promote better practice in their use.

Suggested Citation

  • John Jerrim & Luis Alejandro Lopez-Agudo & Oscar D. Marcenaro-Gutierrez & Nikki Shure, 2017. "What Happens When Econometrics and Psychometrics Collide? An Example Using PISA Data," DoQSS Working Papers 17-04, Department of Quantitative Social Science - UCL Institute of Education, University College London.
  • Handle: RePEc:qss:dqsswp:1704
    as

    Download full text from publisher

    File URL: http://repec.ioe.ac.uk/REPEc/pdf/qsswp1704.pdf
    Download Restriction: no

    References listed on IDEAS

    as
    1. Stefanie Hof, 2014. "Does Private Tutoring Work? The Effectiveness of Private Tutoring: A Nonparametric Bounds Analysis," Economics of Education Working Paper Series 0096, University of Zurich, Department of Business Administration (IBW).
    2. Tommaso Agasisti, 2013. "The efficiency of Italian secondary schools and the potential role of competition: a data envelopment analysis using OECD-PISA2006 data," Education Economics, Taylor & Francis Journals, vol. 21(5), pages 520-544, December.
    3. Cain Polidano & Barbara Hanel & Hielke Buddelmeyer, 2013. "Explaining the socio-economic status school completion gap," Education Economics, Taylor & Francis Journals, vol. 21(3), pages 230-247, July.
    4. Stefanie Hof, 2014. "Does private tutoring work? The effectiveness of private tutoring: a nonparametric bounds analysis," Education Economics, Taylor & Francis Journals, vol. 22(4), pages 347-366, August.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. repec:ebl:ecbull:eb-17-00498 is not listed on IDEAS
    2. repec:eee:ecoedu:v:62:y:2018:i:c:p:230-253 is not listed on IDEAS
    3. Rodríguez-Planas, Núria & Nollenberger, Natalia, 2018. "Let the girls learn! It is not only about math … it's about gender social norms," Economics of Education Review, Elsevier, vol. 62(C), pages 230-253.

    More about this item

    Keywords

    Survey design; Test design; PISA; Weights; Replicate weights; Plausible values;

    JEL classification:

    • I20 - Health, Education, and Welfare - - Education - - - General
    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:qss:dqsswp:1704. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Bilal Nasim). General contact details of provider: http://edirc.repec.org/data/dqioeuk.html .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.