Multilevel Design Parameters to Plan Cluster-Randomized Intervention Studies on Student Achievement in Elementary and Secondary School
Faculty/Professorship: | Longitudinal Educational Research |
Author(s): | Stallasch, Sophie E.; Lüdtke, Oliver; Artelt, Cordula ![]() |
Publisher Information: | Bamberg : Otto-Friedrich-Universität |
Year of publication: | 2022 |
Pages: | 172-206 |
Source/Other editions: | Journal of research on educational effectiveness. - 14 (2021), 1, S. 172-206 |
is version of: | 10.1080/19345747.2020.1823539 |
Year of first publication: | 2021 |
Language(s): | English |
Licence: | Creative Commons - CC BY-NC-ND - Attribution - NonCommercial - NoDerivatives 4.0 International |
URN: | urn:nbn:de:bvb:473-irb-495440 |
Abstract: | To plan cluster-randomized trials with sufficient statistical power todetect intervention effects on student achievement, researchers needmultilevel design parameters, including measures of between-class-room and between-school differences and the amounts of varianceexplained 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 achievementdomains (i.e., mathematics, science, reading). Using representativedata of students attending grades 1–12 from three German longitu-dinal large-scale assessments (3,963 ≤ N ≤ 14,640), we used three-and two-level latent (covariate) models to provide design parametersand corresponding standard errors for a broad array of domain-spe-cific (e.g., mathematics, science, verbal skills) and domain-general(e.g., basic cognitive functions) achievement outcomes. Three covari-ate sets were applied comprising (a) pretest scores, (b) sociodemo-graphic characteristics, and (c) their combination. Design parametersvaried considerably as a function of the hierarchical level, achieve-ment outcome, and grade level. Our findings demonstrate the needto strive for an optimal fit between design parameters and targetresearch context. We illustrate the application of design parametersin power analyses. |
GND Keywords: | Schulleistungsmessung |
Keywords: | Intraclass correlation, explained variance, large-scale assessment, multilevellatent (covariate) model, power analysis |
DDC Classification: | 370 Education |
RVK Classification: | DO 1251 |
Peer Reviewed: | Ja |
International Distribution: | Ja |
Open Access Journal: | Ja |
Type: | Article |
URI: | https://fis.uni-bamberg.de/handle/uniba/49544 |
Release Date: | 20. July 2022 |
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fisba49544.pdf | 2.72 MB | View/Open |

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University of Bamberg
University of Bamberg