Multiple factor analysis with external information on PISA survey data

OECD-PISA survey data include performance measurements, expressed as tables of plausible values, and a variety of socio-biographical information. Such data, if properly modeled, can provide useful insights on the causes of low performing students. A methodology which deals with multivariate sets of plausible values and investigates the effects of context variables without assumptions is required. Here Multiple Factor Analysis with External Information is proposed. Specifically, after defining context variable groupings, a partitioning of the variability structure of the data tables is carried out using projection operators than a simplified Multiple Factor Analysis with bootstrap is performed.