This topic is dedicated to the analysis of PISA database using the R statistical program

Every three years, since 2000, the OECD (Organization for Economic Cooperation and Development) performs a series of tests in a number of countries at national level to 15-years-old students, in order to assess the degree of knowledge in three main groups of areas: science, reading and math. This is the PISA program, whose last edition took place in 2015.

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In the previous article in this series we saw an introduction to PISA data analytics, with examples of functions in R code for sampling, and we talked about the sampling weights, which ponder each student so that it represents a group of individuals with the same characteristics rather than a single student, (remember that PISA aims to assess the effect of educational policies on the whole population of the country, not on individual students). In this article, we will see how to use these weights to calculate estimators from samples and we'll see also how to calculate standard errors of these estimators using replicated weights.

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In the previous article in this series we viewed how to computing standard errors with replicate weights in PISA database, in this article we will take an overview of one of the most controversial points of these studies, the complex system of scores implemented.

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The PISA database contains, in addition to the scores of students, a lot of demographic, socioeconomic and cultural data about them, collected through a series of questionnaires, that allow contextualize the academic results and make studies with a great number of variables. Most of these data are categorical, making the correspondence analysis a particularly appropriate tool to work with them. In this article I will show you how to easily perform this analysis using the ca package of the R program.

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