Multilevel design parameters to plan cluster-randomized intervention studies on student achievement in elementary and secondary school
- To plan cluster-randomized trials with sufficient statistical power to detect intervention effects on student achievement, researchers need multilevel design parameters, including measures of between-classroom and between-school differences and the amounts of variance explained by covariates at the student, classroom, and school level. Previous research has mostly been conducted in the United States, focused on two-level designs, and limited to core achievement domains (i.e., mathematics, science, reading). Using representative data of students attending grades 1-12 from three German longitudinal large-scale assessments (3,963 <= N <= 14,640), we used three- and two-level latent (covariate) models to provide design parameters and corresponding standard errors for a broad array of domain-specific (e.g., mathematics, science, verbal skills) and domain-general (e.g., basic cognitive functions) achievement outcomes. Three covariate sets were applied comprising (a) pretest scores, (b) sociodemographic characteristics, and (c) theirTo plan cluster-randomized trials with sufficient statistical power to detect intervention effects on student achievement, researchers need multilevel design parameters, including measures of between-classroom and between-school differences and the amounts of variance explained by covariates at the student, classroom, and school level. Previous research has mostly been conducted in the United States, focused on two-level designs, and limited to core achievement domains (i.e., mathematics, science, reading). Using representative data of students attending grades 1-12 from three German longitudinal large-scale assessments (3,963 <= N <= 14,640), we used three- and two-level latent (covariate) models to provide design parameters and corresponding standard errors for a broad array of domain-specific (e.g., mathematics, science, verbal skills) and domain-general (e.g., basic cognitive functions) achievement outcomes. Three covariate sets were applied comprising (a) pretest scores, (b) sociodemographic characteristics, and (c) their combination. Design parameters varied considerably as a function of the hierarchical level, achievement outcome, and grade level. Our findings demonstrate the need to strive for an optimal fit between design parameters and target research context. We illustrate the application of design parameters in power analyses.…
Author details: | Sophie E. StallaschORCiDGND, Oliver Lüdtke, Cordula Artelt, Martin BrunnerORCiD |
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DOI: | https://doi.org/10.1080/19345747.2020.1823539 |
ISSN: | 1934-5747 |
ISSN: | 1934-5739 |
Title of parent work (English): | Journal of research on educational effectiveness |
Publisher: | Routledge, Taylor & Francis Group |
Place of publishing: | Abingdon |
Publication type: | Article |
Language: | English |
Date of first publication: | 2021/01/22 |
Publication year: | 2021 |
Release date: | 2024/09/04 |
Tag: | Intraclass correlation; explained variance; large-scale assessment; multilevel latent (covariate) model; power analysis |
Volume: | 14 |
Issue: | 1 |
Number of pages: | 35 |
First page: | 172 |
Last Page: | 206 |
Funding institution: | Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)German Research Foundation (DFG) [392108331] |
Organizational units: | Humanwissenschaftliche Fakultät / Strukturbereich Bildungswissenschaften / Department Erziehungswissenschaft |
DDC classification: | 3 Sozialwissenschaften / 37 Bildung und Erziehung / 370 Bildung und Erziehung |
Peer review: | Referiert |
Publishing method: | Open Access / Hybrid Open-Access |
License (German): | ![]() |