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University of Gothenburg


Center for Comparative Analysis of Educational Achievement. COMPEAT is an infrastructure project, whose general aim is to build databases of international large-scale studies in educational achievement conducted by IEA and OECD before year 2000, and to support secondary analyses of these data.

About the project

During the last 40 years, a large number of studies of student achievement in different school subjects have been conducted by the IEA (the International Association for the Evaluation of Educational Achievement) and the OECD (the Organisation for Economic Co-operation and Development). And the number of participating countries continues to increase. The results from these studies have great impact on educational policy and practice. Data collected in these studies offered a valuable resource for different research purposes. However, there are certain difficulties intact in these data that make data management and analyses more complicated and technique demanding.

  • Complex and stratified cluster sampling design needs advanced statistic modeling techniques to take care of the non-independency in the data;
  • Achievement measures from matrix designs, e.g. students have only partly been administered the same test tasks, needs missing data techniques to take care of the non-responses;
  • Measurement errors in the observed variables needs to be handled by latent variable analyses;
  • Variables observed at a low scale level, usually categorical or ordinal level, needs rescaling techniques such as IRT and factor score approach.
  • Data from the studies before 1990 are more difficult to access, and was processed in a different manner compared to data from studies conducted later;
  • Country-level information are limited;
  • Causal inferences are difficult to draw with cross-sectional data.

The purpose with the project

The potential of these databases have therefore not fully been taken charged of. The purpose of COMPEAT is to create better conditions for secondary analysis by the mean of:

  1. Building databases
    Make available data and documentation from studies before 1990 in updated formats add information to the databases with variables at the national level
  2. Prepare data for analysis
    Create aggregated (country level) and disaggregated (individual level) databases.
    Create new variables from combinations of several variables.
  3. Build analytical competences