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
Campos, D. G., & Scherer, R. (2024). Digital gender gaps in Students’ knowledge, attitudes and skills: an integrative data analysis across 32 Countries. Education and Information Technologies, 29(1), 655-693. https://doi.org/10.1007/s10639-023-12272-9
Campos, D. G., Fütterer, T., Gfrörer, T., Lavelle-Hill, R., Murayama, K., König, L., Hecht, M., Zitzmann, S., & Scherer, R. (2024). Screening Smarter, Not Harder: A Comparative Analysis of Machine Learning Screening Algorithms and Heuristic Stopping Criteria for Systematic Reviews in Educational Research. Educational Psychology Review, 36(1), 19. https://doi.org/10.1007/s10648-024-09862-5
Scherer, R., Siddiq, F., & Nilsen, T. (2024). The potential of international large-scale assessments for meta-analyses in education. Large-scale Assessments in Education, 12(1), 4. https://doi.org/10.1186/s40536-024-00191-1
Campos, D. G., Cheung, M. W.-L., & Scherer, R. (2023). A primer on synthesizing individual participant data obtained from complex sampling surveys: A two-stage IPD meta-analysis approach. Psychological Methods. https://doi.org/10.1037/met0000539
Blömeke, S., Nilsen, T., & Scherer, R. (2021). School Innovativeness Is Associated With Enhanced Teacher Collaboration, Innovative Classroom Practices, and Job Satisfaction. Journal of Educational Psychology, 113(8), 1645-1667. https://doi.org/10.1037/edu0000668
Scherer, R., & Teo, T. (2020). A tutorial on the meta-analytic structural equation modeling of reliability coefficients. Psychological Methods, 25(6), 747-775. https://doi.org/10.1037/met0000261
Siddiq, F., & Scherer, R. (2019). Is there a gender gap? A meta-analysis of the gender differences in students’ ICT literacy. Educational Research Review, 27, 205-217. https://doi.org/10.1016/j.edurev.2019.03.007
Scherer, R., & Siddiq, F. (2019). The relation between students’ socioeconomic status and ICT literacy: Findings from a meta-analysis. Computers & Education, 138, 13-32. https://doi.org/10.1016/j.compedu.2019.04.011