![]() ![]() The analysis functions export MS Excel workbook with separate sheets for estimates, model statistics, analysis information, and the syntax used for the analysis which can be used to replicate or repeat the computations if the data are updated. The most distinctive features, compared to other R packages, are the output export and the graphical user interface (GUI). The number analysis types will grow in future, and new features will be added to the aforementioned ones. Currently, ‘RALSA’ supports the following analysis types: percentages and means, percentiles, benchmarks/proficiency levels, correlations (Pearson or Spearman), linear regression, binary logistic regression. The second set of functions are the analysis functions. TIMSS and PIRLS) while taking care for the user-defined missing values. merging student and teacher data in ICCS and ICILS vs. ‘RALSA’ can also merge data from different respondent types while preventing merging data from respondents which shall not be merged depending on a study’s design (e.g. Another important data preparation function is the variable codebook which prints or saves a codebook for all variables or just for the ones selected by the user. ‘RALSA’ also brings its own variable recoding function which handles the user-defined missing values. ![]() Unlike the usual R handling of missing data, ‘RALSA’ attaches the user-defined missing values to each variable. These are later used by all other data preparation and analysis functions. While converting the data into native R data sets, it attaches the study name, cycle and respondent type as an attribute to the data. It can also convert PISA data prior its 2015 cycle where the data sets are not provided in SPSS file format, but as text files with an SPSS control syntax. The first and most important data preparation feature of ‘RALSA’ is that it converts data from SPSS into native R data sets for further use (data preparation or analysis). The functionalities of the package are organized into two sets: data preparation and data analysis. It supports data from all cycles of a broad range of studies: CivED, ICCS, ICILS, RLII, PIRLS (including PIRLS Literacy and ePIRLS), TIMSS (including TIMSS Numeracy and eTIMSS), TiPi (TIMSS and PIRLS joint study), TIMSS Advanced, SITES, TEDS-M, PISA, PISA for Development, TALIS, and TALIS Starting Strong Survey (also known as TALIS 3S). It was built for user experience and is suitable even for analysts having no prior experience with R. The R Analyzer for Large-Scale Assessments (‘RALSA’) (Mirazchiyski & INERI, 2021) is a new package which came to life in November 2020. ![]() All of these packages can analyze ILSAs’ data, handling the statistical issues stemming from the complex sampling and assessment designs the studies have. not proprietary) and open-source tools like the R packages ‘intsvy’ (Caro & Biecek, 2019), ‘BIFIEsurvey’ (BIFIE et al., 2019) and ‘EdSurvey’ (Bailey et al., 2020) are available as well. ![]() The first available and most commonly used package is the IEA IDB Analyzer (IEA, 2020). Several statistical packages are available for analyzing data stemming from international large-scale assessments and surveys (ILSAs). By Plamen Vladkov Mirazchiyski, International Educational Research and Evaluation Institute (INERI), Slovenia ![]()
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