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Causal inferences with cross-sectional large scale assessment data

Researchers and policy makers in education want to know if anything can be done to improve achievement overall and for particular groups of test takers. This, in turn, has motivated interest in making connections between a host of potential causes and achievement. 

Results, methodological aspects and advancements of the Programme for the International Assessment of Adult Competencies (PIAAC)

This special issue presents results of the PIAAC study regarding socio-economic differences such as education and gender. Results imply recommendations for policy actions to approach problems associated with skill inequality.

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IERI Monograph Series Archive

Large-scale Assessments in Education was published from 2008 to 2012 as the "IERI Monograph Series". Archive content of the IERI Monograph Series is available online at ierinstitute.org/dissemination-area.html

Aims and scope

Large-scale Assessments in Education is a joint publication of the International Association for the Evaluation of Educational Achievement (IEA) and Educational Testing Service (ETS). The articles in this journal contribute to the science of large-scale assessments, help disseminate state-of-the-art information about empirical research using these databases and make the results available to policy makers and researchers around the world.

Articles suitable for publication in the journal focus on improving the science of large-scale assessments and make use of data collected by programs, such as: IEA’s TIMSS, PIRLS, ICCS, and SITES; U.S.-NAEP; OECD’s PISA, PIAAC and TALIS; IALS, ALL and others. If you have questions or concerns about whether your article adheres to the purpose of this journal, please contact the editors directly.

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Society affiliation

          

Large-scale Assessments in Education is fully sponsored by the IEA-ETS Research Institute, a collaborative effort between the Research & Development Division at ETS and the IEA Data Processing and Research Center that focuses on improving the science of large-scale assessments. Authors do not need to pay an article-processing charge.