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Meta-Analyses of International Large-Scale Assessment Data (Project MILSA)

Idea in Brief

Background

Identifying the success and challenges of education systems, policies, and programs heavily relies on the evidence provided by empirical research studies. International Large-Scale Assessments (ILSAs), such as TIMSS, PIRLS, PISA, TALIS, PIAAC, and ICILS, aim at making visible key educational inputs, processes, and outcomes, including student achievement, classroom characteristics, and school climate, and thus inform educational practice and policy-making. In fact, given their wealth of variables and indicators and their sampling designs, ILSAs provide unique resources and opportunities for researchers to generate and test hypotheses about the relations among educationally relevant constructs, progress over time, and differences between groups of students, schools, or countries. However, given the complexity and the sheer size of ILSA data sets, standard approaches to analysing them and, ultimately, provide meaningful results face their limits.

Goals

  • Develop and evaluate analytic approaches to synthesizing specific results of ILSAs using meta-analyses.
  • Conduct meta-analyses that address educationally relevant research questions with ILSA data.
  • Provide tools and guidelines for researchers who wish to synthesize the results of ILSAs for a specific research question.

Projects

  • Principles and Practices to Synthesize Research Evidence from Complex Survey Data
  • Principles and practices of including ILSA data in meta-analyses in education
  • Meta-analysis of gender differences in digital literacy
  • Meta-analysis of the relation between SES and digital literacy
  • Synthesizing the relations among team innovativeness, teacher collaboration, job satisfaction, and instructional practices across 48 countries using TALIS 2018 data

Publications

Tags: Meta-analysis, ILSA, Comparative and international studies
Published Aug. 20, 2020 9:31 PM - Last modified Aug. 20, 2020 9:34 PM