Multilevel Design Parameters to Plan Cluster-Randomized Intervention Studies on Student Achievement in Elementary and Secondary School

Autor/inn/en	Stallasch, Sophie E.; Lüdtke, Oliver; Artelt, Cordula; Brunner, Martin
Titel	Multilevel Design Parameters to Plan Cluster-Randomized Intervention Studies on Student Achievement in Elementary and Secondary School
Quelle	In: Journal of Research on Educational Effectiveness, 14 (2021) 1, S.172-206 (35 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1934-5747
DOI	10.1080/19345747.2020.1823539
Schlagwörter	Foreign Countries; Randomized Controlled Trials; Intervention; Educational Research; Academic Achievement; Elementary School Students; Secondary School Students; Hierarchical Linear Modeling; Statistical Analysis; Research Design; Error of Measurement; Pretests Posttests; Scores; Student Characteristics; Germany + Suchen Sie Ihr Suchwort? Ausland; Bildungsforschung; Pädagogische Forschung; Schulleistung; Sekundarschüler; Statistische Analyse; Forschungsdesign; Messfehler; Deutschland
Abstract	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 [less than or equal to] N [less than or equal to] 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. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01