The blind side: Exploring item variance in PISA 2018 cognitive domains

References

• Ahmed, A., & Pollitt, A. (2007). Improving the quality of contextualized questions: An experimental investigation of focus. Assessment in Education: Principles, Policy & Practice, 14(2), 201–232. https://doi.org/10.1080/09695940701478909
   Google Scholar
• Bates, D., Mächler, M., Bolker, B., & Walker, S. (2015). Fitting linear mixed-effects models using lme4. Journal of Statistical Software, 67(1), 1–48. https://doi.org/10.18637/jss.v067.i01
   Web of Science ®Google Scholar
• Bodovski, K., Byun, S., Chykina, V., & Chung, H. (2017). Searching for the golden model of education: Cross-national analysis of math achievement. Economics of Education Review, 47(5), 722–741. https://doi.org/10.1080/03057925.2016.1274881
   Google Scholar
• Briggs, D. C., & Wilson, M. (2007). Generalizability in Item Response modeling. Journal of Educational Measurement, 44(2), 131–155. https://doi.org/10.1111/j.1745-3984.2007.00031.x
   Web of Science ®Google Scholar
• Daus, S., Nilsen, T., & Braeken, J. (2019). Exploring content knowledge: Country profile of science strengths and weaknesses in TIMSS. Possible implications for educational professionals and science research. Scandinavian Journal of Educational Research, 63(7), 1102–1120. https://doi.org/10.1080/00313831.2018.1478882
   Web of Science ®Google Scholar
• De Boeck, P. (2008). Random item IRT models. Psychometrika, 73(4), 533–559. https://doi.org/10.1007/s11336-008-9092-x
   Web of Science ®Google Scholar
• Eijkelhof, H. M. C., Kordes, J. H., & Savelsbergh, E. R. (2013). Implications of PISA outcomes for science curriculum reform in the Netherlands. In M. Prenzel, M. Kobarg, K. Schöps, & S. Rönnebeck (Eds.), Research on PISA (pp. 7–21). Springer Netherlands. https://doi.org/10.1007/978-94-007-4458-5_1
   Google Scholar
• El Masri, Y. H., Ferrara, S., Foltz, P. W., & Baird, J.-A. (2017). Predicting item difficulty of science national curriculum tests: The case of key stage 2 assessments. The Curriculum Journal, 28(1), 59–82. https://doi.org/10.1080/09585176.2016.1232201
   Web of Science ®Google Scholar
• Hanushek, E. A., & Wöeßmann, L. (2006). Does early tracking affect educational inequality and performance? Differences-in-differences evidence across countries. Economic Journal, 116(115), C63–C76. https://doi.org/10.1111/j.1468-0297.2006.01076.x
   Google Scholar
• Hanushek, E. A., Link, S., & Wöeßmann, L. (2017). Does school autonomy make sense everywhere? Panel estimates from PISA. Journal of Development Economics, 104, 212–232. https://doi.org/10.1016/j.jdeveco.2012.08.002
   Google Scholar
• Hopfenbeck, T. N., Lenkeit, J., El Masri, Y., Cantrell, K., Ryan, J., & Baird, J.-A. (2018). Lessons learned from PISA: A systematic review of peer-reviewed articles on the Programme for International Student Assessment. Scandinavian Journal of Educational Research, 62(3), 333–353. https://doi.org/10.1080/00313831.2016.1258726
   Web of Science ®Google Scholar
• Kline, R. B. (2016). Principles and practice of structural equation modeling (4th ed.). Guilford Press.
   Google Scholar
• Le Hebel, F., Montpied, P., Tiberghien, A., & Fontanieu, V. (2017). Sources of difficulty in assessment: Example of PISA science items. International Journal of Science Education, 39(4), 468–487. https://doi.org/10.1080/09500693.2017.1294784
   Web of Science ®Google Scholar
• Lundahl, L. (2016). Equality, inclusion and marketization of Nordic education: Introductory notes. Research in Comparative and International Education, 11(1), 3–12. https://doi.org/10.1177/1745499916631059
   Web of Science ®Google Scholar
• Mullis, I. V. S., Martin, M., Foy, P., Kelly, D., & Fishbein, B. (2020). TIMSS 2019 international results in mathematics and science. TIMSS & PIRLS International Study Center.
   Google Scholar
• OECD. (2019). PISA 2018 results (Volume 1): What students know and can do. https://doi.org/10.1787/5f07c754-en
   Google Scholar
• OECD. (2020) . PISA 2018 technical report, PISA.
   Google Scholar
• R Core Team. (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing. https://www.R-project.org/
   Google Scholar
• Rijmen, F., Tuerlinckx, F., De Boeck, P., & Kuppens, P. (2003). A nonlinear mixed model framework for item response theory. Psychological Methods, 8(2), 185–205. https://doi.org/10.1037/1082-989X.8.2.185
   PubMed Web of Science ®Google Scholar
• Rutkowski, L., Rutkowski, D., & Liaw, Y.-L. (2019). The existence and impact of floor effects for low-performing PISA participants. Assessment in Education: Principles, Policy & Practice, 26(6), 643–664. https://doi.org/10.1080/0969594X.2019.1577219
   Google Scholar
• Strello, A., Strietholt, R., Steinmann, I., & Siepmann, C. (2021). Early tracking and different types of inequalities in achievement: Difference-in-differences evidence from 20 years of large-scale assessments. Educational Assessment, Evaluation and Accountability, 33(1), 139–167. https://doi.org/10.1007/s11092-020-09346-4
   Web of Science ®Google Scholar
• Tukey, J. W. (1980). We need both exploratory and confirmatory. The American Statistician, 34(1), 23–25. https://doi.org/10.2307/2682991
   Web of Science ®Google Scholar
• Van den Noortgate, W., De Boeck, P., & Meulders, M. (2003). Cross-classification multilevel logistic models in psychometrics. Journal of Educational and Behavioral Statistics, 28(4), 369–386. https://doi.org/10.3102/10769986028004369
   Web of Science ®Google Scholar
• Van Huizen, T., & Plantenga, J. (2018). Do children benefit from universal early childhood education and care? A meta-analysis of evidence from natural experiments. Economics of Education Review, 66(2), 206–222. https://doi.org/10.1016/j.econedurev.2018.08.001
   Google Scholar
• Yang Hansen, K., Gustafsson, J.-E., & Rosén, M. (2014). School performance differences and policy variations in Finland, Norway and Sweden. In K. Yang Hansen (Ed.), Northern lights on TIMSS and PIRLS 2011: Differences and similarities in the Nordic countries (pp. 25–47). Nordic Council of Ministers.
   Google Scholar
• Zwitser, R. J., Glaser, S. S. F., & Maris, G. (2017). Monitoring countries in a changing world: A new look at DIF in international surveys. Psychometrika, 82(1), 210–232. https://doi.org/10.1007/s11336-016-9543-8
   PubMed Web of Science ®Google Scholar