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ILSA Data Analysis with R Packages

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Part of the Lecture Notes in Networks and Systems book series (LNNS,volume 363)


High volume and special structure International Large-Scale Assessment data such as PISA (Programme for International Student Assessment), TIMSS (Trends in International Mathematics and Science Study), and others are of interest to social scientists around the world. Such data can be analysed using commercial software such as SPSS, SAS, Mplus, etc. However, the use of open-source R software for statistical calculations has recently increased in popularity. To encourage the social sciences to use open source R software, we overview the possibilities of five packages for statistical analysis of International Large-Scale Assessment data: BIFIEsurvey, EdSurvey, intsvy, RALSA, and svyPVpack. We test and compare the packages using PISA and TIMSS data. We conclude that each package has its advantages and disadvantages. To conduct a comprehensive data analysis of International Large-Scale Assessment surveys one might require to use more than one package.


  • ILSA data
  • R packages
  • Statistical analysis

This project has received funding from European Social Fund (project No. DOTSUT-39 (09.3.3-LMT-K-712-01-0018)/LSS-250000-57) under grant agreement with the Research Council of Lithuania (LMTLT).

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  • DOI: 10.1007/978-3-030-92666-3_23
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  1. Bailey, P., et al.: EdSurvey: Analysis of NCES Education Survey and Assessment Data. R package version 2.6.9 (2021).

  2. Caro, D.H., Biecek, P.: intsvy: an R package for analyzing international large-scale assessment data. J. Stat. Softw. 81(7), 1–44 (2017).

  3. Mirazchiyski, P.V., INERI: RALSA: R Analyzer for Large-Scale Assessments. R package version 0.90.3 (2021).

  4. Reif, M., Peterbauer, J.: svyPVpack: a package for complex surveys including plausible values. R package version 0.1-1 (2014).

  5. Robitzsch, A., Oberwimmer, K.: BIFIEsurvey: tools for survey statistics in educational assessment. R package version 3.3-12 (2019).

  6. Watanabe, R.: PISA Data Analysis Manual SPSS, 2nd edn. OECD, Paris (2009)

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Correspondence to Laura Ringienė .

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Ringienė, L., Žilinskas, J., Jakaitienė, A. (2022). ILSA Data Analysis with R Packages. In: Le Thi, H.A., Pham Dinh, T., Le, H.M. (eds) Modelling, Computation and Optimization in Information Systems and Management Sciences. MCO 2021. Lecture Notes in Networks and Systems, vol 363. Springer, Cham.

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