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Monitoring Countries in a Changing World: A New Look at DIF in International Surveys

Author

Listed:
  • Robert J. Zwitser

    (University of Amsterdam)

  • S. Sjoerd F. Glaser

    (University of Amsterdam)

  • Gunter Maris

    (University of Amsterdam
    Cito Institute for Educational Measurement)

Abstract

This paper discusses the issue of differential item functioning (DIF) in international surveys. DIF is likely to occur in international surveys. What is needed is a statistical approach that takes DIF into account, while at the same time allowing for meaningful comparisons between countries. Some existing approaches are discussed and an alternative is provided. The core of this alternative approach is to define the construct as a large set of items, and to report in terms of summary statistics. Since the data are incomplete, measurement models are used to complete the incomplete data. For that purpose, different models can be used across countries. The method is illustrated with PISA’s reading literacy data. The results indicate that this approach fits the data better than the current PISA methodology; however, the league tables are nearly identical. The implications for monitoring changes over time are discussed.

Suggested Citation

  • Robert J. Zwitser & S. Sjoerd F. Glaser & Gunter Maris, 2017. "Monitoring Countries in a Changing World: A New Look at DIF in International Surveys," Psychometrika, Springer;The Psychometric Society, vol. 82(1), pages 210-232, March.
    • Handle: RePEc:spr:psycho:v:82:y:2017:i:1:d:10.1007_s11336-016-9543-8
    DOI: 10.1007/s11336-016-9543-8
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    References listed on IDEAS

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    1. Geoff Masters, 1982. "A rasch model for partial credit scoring," Psychometrika, Springer;The Psychometric Society, vol. 47(2), pages 149-174, June.
    2. Timo Bechger & Gunter Maris, 2015. "A Statistical Test for Differential Item Pair Functioning," Psychometrika, Springer;The Psychometric Society, vol. 80(2), pages 317-340, June.
    3. Maarten Marsman & Gunter Maris & Timo Bechger & Cees Glas, 2016. "What can we learn from Plausible Values?," Psychometrika, Springer;The Psychometric Society, vol. 81(2), pages 274-289, June.
    4. Svend Kreiner & Karl Christensen, 2014. "Analyses of Model Fit and Robustness. A New Look at the PISA Scaling Model Underlying Ranking of Countries According to Reading Literacy," Psychometrika, Springer;The Psychometric Society, vol. 79(2), pages 210-231, April.
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    Cited by:

    1. Alexander Robitzsch, 2020. "L p Loss Functions in Invariance Alignment and Haberman Linking with Few or Many Groups," Stats, MDPI, vol. 3(3), pages 1-38, August.
    2. Jeanne A. Teresi & Chun Wang & Marjorie Kleinman & Richard N. Jones & David J. Weiss, 2021. "Differential Item Functioning Analyses of the Patient-Reported Outcomes Measurement Information System (PROMIS®) Measures: Methods, Challenges, Advances, and Future Directions," Psychometrika, Springer;The Psychometric Society, vol. 86(3), pages 674-711, September.
    3. Alexander Robitzsch & Oliver Lüdtke, 2022. "Mean Comparisons of Many Groups in the Presence of DIF: An Evaluation of Linking and Concurrent Scaling Approaches," Journal of Educational and Behavioral Statistics, , vol. 47(1), pages 36-68, February.
    4. Daniele, Vittorio, 2021. "Socioeconomic inequality and regional disparities in educational achievement: The role of relative poverty," Intelligence, Elsevier, vol. 84(C).

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