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Articles

Conditioning: how background variables can influence PISA scores

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Pages 632-652 | Received 07 Mar 2021, Accepted 23 Aug 2022, Published online: 24 Sep 2022

References

  • Caro, D. H., & Biecek, P. (2017). Intsvy: An R package for analyzing international large-scale assessment data. Journal of Statistical Software, 81(1), 1–44. https://doi.org/10.18637/jss.v081.i07
  • Egelund, N. (2008). The value of international comparative studies of achievement–a Danish perspective. Assessment in Education: Principles, Policy and Practice, 15(3), 245–251. https://doi.org/10.1080/09695940802417400
  • Eivers, E. (2010). PISA: Issues in implementation and interpretation. The Irish Journal of Education/Iris Eireannach an Oideachais, 38, 94–118. https://www.jstor.org/stable/20789130
  • El Masri, Y. H., Baird, J.-A., & Graesser, A. (2016). Language effects in international testing: The case of PISA 2006 science items. Assessment in Education: Principles, Policy & Practice, 23(4), 427–455. https://doi.org/10.1080/0969594X.2016.1218323
  • Ertl, H. (2006). Educational standards and the changing discourse on education: The reception and consequences of the PISA study in Germany. Oxford Review of Education, 32(5), 619–634. https://doi.org/10.1080/03054980600976320
  • Fernandez-Cano, A. (2016). A methodological critique of the PISA evaluations. Relieve, 22(1), art. M15. http://dx.doi.org/10.7203/relieve.22.1.8806
  • Freitas, P., Nunes, L. C., Balcão Reis, A., Seabra, C., & Ferro, A. (2016). Correcting for sample problems in PISA and the improvement in Portuguese students’ performance. Assessment in Education: Principles, Policy & Practice, 23(4), 456–472. https://doi.org/10.1080/0969594X.2015.1105784
  • Gamboa, L. F., & Waltenberg, F. D. (2012). Inequality of opportunity for educational achievement in Latin America: Evidence from PISA 2006–2009. Economics of Education Review, 31(5), 694–708. https://doi.org/10.1016/j.econedurev.2012.05.002
  • Gillis, S., Polesel, J., & Wu, M. (2016). PISA Data: Raising concerns with its use in policy settings. The Australian Educational Researcher, 43(1), 131–146. https://doi.org/10.1007/s13384-015-0183-2
  • Goldstein, H. (2017). Measurement and evaluation issues with PISA. In L. Volante (Ed.), The PISA effect on global educational governance (pp. 49–58). Routledge.
  • Grek, S. (2009). Governing by numbers: The PISA ‘effect’ in Europe. Journal of Education Policy, 24(1), 23–37. https://doi.org/10.1080/02680930802412669
  • Gromada, A., Rees, G., Chzhen, Y., & Cuesta, J. (2018). Measuring inequality in children’s education in rich countries. Innocenti Working Papers. https://doi.org/10.18356/5f90f95e-en
  • Hopmann, S., Brinek, G., & Retzl, M. (2007). PISA according to PISA: Does PISA keep what it promises? (Vol. 6). LIT Verlag.
  • Jerrim, J., Parker, P., Choi, A., Chmielewski, A. K., Sälzer, C., & Shure, N. (2018). How robust are cross-country comparisons of PISA scores to the scaling model used? Educational Measurement: Issues and Practice, 37(4), 28–39. https://doi.org/10.1111/emip.12211
  • Kankaraš, M., & Moors, G. (2014). Analysis of cross-cultural comparability of PISA 2009 scores. Journal of Cross-Cultural Psychology, 45(3), 381–399. https://doi.org/10.1177/0022022113511297
  • Kreiner, S., & Christensen, K. (2014). Analyses of model fit and robustness. A new look at the PISA scaling model underlying ranking of countries according to reading literacy. Psychometrika, 79(2), 210–231. https://doi.org/10.1007/s11336-013-9347-z
  • Meyer, H.-D. (2014). The OECD as pivot of the emerging global educational accountability regime: How accountable are the accountants? Teachers College Record, 116(9), 1–20. https://doi.org/10.1177/2F016146811411600907
  • Micklewright, J., Schnepf, S. V., & Skinner, C. (2012). Non‐response biases in surveys of school children: The case of the English Programme for International Student Assessment (PISA) samples. Journal of the Royal Statistical Society: Series A (Statistics in Society), 175(4), 915–938. https://doi.org/10.1111/j.1467-985X.2012.01036.x
  • Mislevy, R. J. (1991). Randomization-based inference about latent variables from complex samples. Psychometrika, 56(2), 177–196. https://doi.org/10.1007/BF02294457
  • Mislevy, R. J., Beaton, A. E., Kaplan, B., & Sheehan, K. M. (1992). Estimating population characteristics from sparse matrix samples of item responses. Journal of Educational Measurement, 29(2), 133–161. https://doi.org/10.1111/j.1745-3984.1992.tb00371.x
  • OECD. (2014a) . PISA 2012 technical report.
  • OECD. (February 2014b). What students know and can do: Student performance in mathematics, reading and science, Rev.
  • Oppedisano, V., & Turati, G. (2015). What are the causes of educational inequality and of its evolution over time in Europe? Education Economics, 23(1), 3–24. https://doi.org/10.1080/09645292.2012.736475
  • R Core Team. (2019). R: A language and environment for statistical computing. R Foundation. www.R-project.org
  • Robitzsch, A., Kiefer, T., & Wu, M. (2018). TAM: Test analysis modules (R package version 3.1-45). https://CRAN.R-project.org/package=TAM
  • Rubin, D. B. (1987). Multiple imputation for nonresponse in surveys. Wiley.
  • Rutkowski, L. (2014). Sensitivity of achievement estimation to conditioning model misclassification. Applied Measurement in Education, 27(2), 115–132. https://doi.org/10.1080/08957347.2014.880440
  • Rutkowski, L., & Rutkowski, D. (2016). A call for a more measured approach to reporting and interpreting PISA results. Educational Researcher, 45(4), 252–257. https://doi.org/10.3102/0013189X16649961
  • Sellar, S., & Lingard, B. (2013). Looking East: Shanghai, PISA 2009 and the reconstitution of reference societies in the global education policy field. Comparative Education, 49(4), 464–485. https://doi.org/10.1080/03050068.2013.770943
  • Takayama, K. (2008). The politics of international league tables: PISA in Japan’s achievement crisis debate. Comparative Education, 44(4), 387–407. https://doi.org/10.1080/03050060802481413
  • van Rijn, P. (2018, November 7). Basic principles of population modelling. IERI Academy hosted by CARPE.
  • Wu, M. (2005). The role of plausible values in large-scale surveys. Measurement, Evaluation, and Statistical Analysis, 31(2), 114–128. https://doi.org/10.1016/j.stueduc.2005.05.005
  • Wuttke, J. (2007). Uncertainty and bias in PISA. In S. T. Hopmann, G. Brinek, & M. Retzl (Eds.), PISA according to PISA: Does PISA keep what it promises (pp. 241–263). LIT Verlag.
  • Zieger, L. (2021). Code for “Conditioning: How background variables can influence PISA scores.” osf.io/8fzns

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