A Test for Cluster Bias: Detecting Violations of Measurement Invariance across Clusters in Multilevel Data

Autor/inn/en	Jak, Suzanne; Oort, Frans J.; Dolan, Conor V.
Titel	A Test for Cluster Bias: Detecting Violations of Measurement Invariance across Clusters in Multilevel Data
Quelle	In: Structural Equation Modeling: A Multidisciplinary Journal, 20 (2013) 2, S.265-282 (18 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1070-5511
DOI	10.1080/10705511.2013.769392
Schlagwörter	Statistical Bias; Measurement; Structural Equation Models; Hierarchical Linear Modeling; Elementary School Students; Student Attitudes; Mathematics; Simulation; Statistical Analysis + Suchen Sie Ihr Suchwort? Messverfahren; Schülerverhalten; Mathematik; Simulation program; Simulationsprogramm; Statistische Analyse
Abstract	We present a test for cluster bias, which can be used to detect violations of measurement invariance across clusters in 2-level data. We show how measurement invariance assumptions across clusters imply measurement invariance across levels in a 2-level factor model. Cluster bias is investigated by testing whether the within-level factor loadings are equal to the between-level factor loadings, and whether the between-level residual variances are zero. The test is illustrated with an example from school research. In a simulation study, we show that the cluster bias test has sufficient power, and the proportions of false positives are close to the chosen levels of significance. (Contains 4 tables and 3 figures.) (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10