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 ![]() |
Title of the Journal: | Journal of research on educational effectiveness |
ISSN: | 1934-5747 |
Corporate Body: | Society for Research on Educational Effectiveness (SREE) |
Publisher Information: | Abingdon : Routledge, Taylor & Francis |
Year of publication: | 2021 |
Volume: | 14 |
Issue: | 1 |
Pages: | 172-206 |
Language(s): | English |
DOI: | 10.1080/19345747.2020.1823539 |
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/52516 |
Release Date: | 21. December 2021 |

originated at the
University of Bamberg
University of Bamberg