Alternatives to Multilevel Modeling for the Analysis of Clustered Data

Autor/in	Huang, Francis L.
Titel	Alternatives to Multilevel Modeling for the Analysis of Clustered Data
Quelle	In: Journal of Experimental Education, 84 (2016) 1, S.175-196 (22 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0022-0973
DOI	10.1080/00220973.2014.952397
Schlagwörter	Multivariate Analysis; Hierarchical Linear Modeling; Sample Size; Error of Measurement; Statistical Significance; Data; Monte Carlo Methods; Statistical Bias; Regression (Statistics); Context Effect; Educational Research + Suchen Sie Ihr Suchwort? Multivariate Analyse; Messfehler; Daten; Monte-Carlo-Methode; Regression; Regressionsanalyse; Bildungsforschung; Pädagogische Forschung
Abstract	Multilevel modeling has grown in use over the years as a way to deal with the nonindependent nature of observations found in clustered data. However, other alternatives to multilevel modeling are available that can account for observations nested within clusters, including the use of Taylor series linearization for variance estimation, the design effect adjusted standard errors approach, and fixed effects modeling. Using 1,000 replications of 12 conditions with varied Level 1 and Level 2 sample sizes, the author compared parameter estimates, standard errors, and statistical significance using various alternative procedures. Results indicate that several acceptable procedures can be used in lieu of or together with multilevel modeling, depending on the type of research question asked and the number of clusters under investigation. Guidelines for applied researchers are discussed. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01