A Latent Cluster-Mean Approach to the Contextual Effects Model with Missing Data

Autor/inn/en	Shin, Yongyun; Raudenbush, Stephen W.
Titel	A Latent Cluster-Mean Approach to the Contextual Effects Model with Missing Data
Quelle	In: Journal of Educational and Behavioral Statistics, 35 (2010) 1, S.26-53 (28 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	Weitere Informationen
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1076-9986
DOI	10.3102/1076998609345252
Schlagwörter	Academic Achievement; National Surveys; Computation; Inferences; High Schools; Models; Regression (Statistics); Multivariate Analysis; Achievement Gains; Mathematics; Error of Measurement; Mathematics Achievement; Public Schools; Catholic Schools; Socioeconomic Status; Equations (Mathematics); Bayesian Statistics; Maximum Likelihood Statistics + Suchen Sie Ihr Suchwort? Schulleistung; Inference; Inferenz; High school; Oberschule; Analogiemodell; Regression; Regressionsanalyse; Multivariate Analyse; Achievement gain; Leistungssteigerung; Mathematik; Messfehler; Mathmatics sikills; Mathmatics achievement; Mathematical ability; Mathematische Kompetenz; Public school; Öffentliche Schule; Katholische Schule; Socio-economic status; Sozioökonomischer Status; Equations; Mathematics; Gleichungslehre
Abstract	In organizational studies involving multiple levels, the association between a covariate and an outcome often differs at different levels of aggregation, giving rise to widespread interest in "contextual effects models." Such models partition the regression into within- and between-cluster components. The conventional approach uses each cluster's sample average of the covariate as a regressor to identify the between-cluster component of the regression. This procedure, however, yields biased estimates of contextual effects unless the cluster sizes are large. Moreover, bias in estimation of such contextual coefficients in turn introduces bias in estimated coefficients of other correlated cluster-level covariates. Missing data further complicate valid inferences. This article proposes an alternative approach that conditions on the latent "true" cluster means of covariates having contextual effects while taking into account ignorable missing data with a general missing pattern at each level. The proposed model may include random coefficients. We compare inferences under different approaches in estimation of a contextual effects model using data from two national surveys of high school achievement. (Contains 6 tables and 1 figure.) (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10