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  ; Brunner, Martin
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