Intraclass Correlations and Covariate Outcome Correlations for Planning 2 and 3 Level Cluster Randomized Experiments in Education

Autor/inn/en	Hedges, Larry V.; Hedberg, Eric C.
Titel	Intraclass Correlations and Covariate Outcome Correlations for Planning 2 and 3 Level Cluster Randomized Experiments in Education
Quelle	(2013), (40 Seiten) PDF als Volltext (1); PDF als Volltext kostenfreie Datei (2) Verfügbarkeit
Zusatzinformation	Weitere Informationen
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
DOI	10.1177/0193841X14529126
Schlagwörter	Quantitative Daten; Correlation; Multivariate Analysis; Educational Experiments; Academic Achievement; Statistical Analysis; Hierarchical Linear Modeling; Comparative Analysis; School Districts; Schools; Elementary Secondary Education; Arizona; Arkansas; Colorado; Florida; Kentucky; Louisiana; Massachusetts; North Carolina; West Virginia; Wisconsin; Florida Comprehensive Assessment Test; Massachusetts Comprehensive Assessment System; Wisconsin Knowledge and Concepts Examinations + Suchen Sie Ihr Suchwort? Korrelation; Multivariate Analyse; Schulversuch; Schulleistung; Statistische Analyse; School district; Schulbezirk; School; Schule; Master-Studiengang
Abstract	Background: Cluster randomized experiments that assign intact groups such as schools or school districts to treatment conditions are increasingly common in educational research. Such experiments are inherently multilevel designs whose sensitivity (statistical power and precision of estimates) depends on the variance decomposition across levels. This variance decomposition is usually summarized by the intraclass correlation structure and, if covariates are used, the effectiveness of the covariates in explaining variation at each level of the design. Objectives: This paper provides a compilation of school and district level intraclass correlation values of academic achievement and related covariate effectiveness based on state longitudinal data systems. These values are designed to be used for planning group-randomized experiments in education. The use of these values to compute statistical power and plan 2 and 3 level group randomized experiments is illustrated. Research Design: We fit several hierarchical linear models to state data by grade and subject to estimate intraclass correlations and covariate effectiveness. We then compare our average of state estimates with the national work by Hedges and Hedberg (2007). [This article was published in "Educational Review," v37 n6 p445-489 Dec 2013, see EJ1034857.] (As Provided).
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01