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Abstract This paper presents the R Analyzer for Large-Scale Assessments (RALSA), a newly developed R package for analyzing data from studies using complex sampling and assessment designs. Such studies are, for example, the IEA’s Trends in International Mathematics and Science Study and the OECD’s Programme for International Student Assessment. The package covers all cycles from a broad range of studies. The paper presents the architecture of the package, the overall workflow and illustrates some basic analyses using it. The package is open-source and free of charge. Other software packages for analyzing large-scale assessment data exist, some of them are proprietary, others are open-source. However, RALSA is the first comprehensive R package, designed for the user experience and has some distinctive features. One innovation is that the package can convert SPSS data from large scale assessments into native R data sets. It can also do so for PISA data from cycles prior to 2015, where the data is provided in tab-delimited text files along with SPSS control syntax files. Another feature is the availability of a graphical user interface, which is also written in R and operates in any operating system where a full copy of R can be installed. The output from any analysis function is written into an MS Excel workbook with multiple sheets for the estimates, model statistics, analysis information and the calling syntax itself for reproducing the analysis in future. The flexible design of RALSA allows for the quick addition of new studies, analysis types and features to the existing ones.

International large-scale assessments (ILSAs) provide invaluable information for researchers and policy makers. Analysis of their data, however, requires methods that go beyond the usual analysis techniques assuming simple random sampling. Several software packages that serve this purpose are available. One such is the R Analyzer for Large-Scale Assessments (RALSA), a newly developed R package. The package can work with data from a large number of ILSAs. It was designed for user experience and is suitable for analysts who lack technical expertise and/or familiarity with the R programming language and statistical software. This paper presents the technical aspects of RALSA—the overall design and structure of the package, its internal organization, and the structure of the analysis and data preparation functions. The use of the data.table package for memory efficiency, speed, and embedded computations is explained through examples. The central aspect of the paper is the utilization of code reuse practices to the achieve consistency, efficiency, and safety of the computations performed by the analysis functions of the package. The comprehensive output system to produce multi-sheet MS Excel workbooks is presented and its workflow explained. The paper also explains how the graphical user interface is constructed and how it is linked to the data preparation and analysis functions available in the package.

This paper presents the R Analyzer for Large-Scale Assessments (RALSA), a newly developed R package for analyzing data from studies using complex sampling and assessment designs. Such studies are, for example, the IEA's Trends in International Mathematics and Science Study and the OECD's Programme for International Student Assessment. The package covers all cycles from a broad range of studies. The paper presents the architecture of the package, the overall workflow and illustrates some basic analyses using it. The package is open-source and free of charge. Other software packages for analyzing large-scale assessment data exist, some of them are proprietary, others are open-source. However, RALSA is the first comprehensive R package, designed for the user experience and has some distinctive features. One innovation is that the package can convert SPSS data from large scale assessments into native R data sets. It can also do so for PISA data from cycles prior to 2015, where the data is provided in tab-delimited text files along with SPSS control syntax files. Another feature is the availability of a graphical user interface, which is also written in R and operates in any operating system where a full copy of R can be installed. The output from any analysis function is written into an MS Excel workbook with multiple sheets for the estimates, model statistics, analysis information and the calling syntax itself for reproducing the analysis in future. The flexible design of RALSA allows for the quick addition of new studies, analysis types and features to the existing ones. [ABSTRACT FROM AUTHOR]