A Hierarchical Linear Model with Factor Analysis Structure at Level 2

Autor/inn/en	Miyazaki, Yasuo; Frank, Kenneth A.
Titel	A Hierarchical Linear Model with Factor Analysis Structure at Level 2
Quelle	In: Journal of Educational and Behavioral Statistics, 31 (2006) 2, S.125-156 (32 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1076-9986
Schlagwörter	Methods; Factor Analysis; Computation; Causal Models; Vertical Organization; Theory Practice Relationship; Matrices; Maximum Likelihood Statistics; Vocabulary Development + Suchen Sie Ihr Suchwort? Method; Methode; Faktorenanalyse; Kausalanalyse; Theorie-Praxis-Beziehung; Matrizenrechnung; Wortschatzarbeit
Abstract	In this article the authors develop a model that employs a factor analysis structure at Level 2 of a two-level hierarchical linear model (HLM). The model (HLM2F) imposes a structure on a deficient rank Level 2 covariance matrix [tau], and facilitates estimation of a relatively large [tau] matrix. Maximum likelihood estimators are derived via the Fisher scoring algorithm. An application of HLM2F to early vocabulary growth data illustrates the utility of the methodology. The methodology shown in this article has several advantages over the current two-level HLM (HLM2). Those include: (a) a parsimonious model that facilitates interplay between the theory and empirical results, (b) estimation of a deficient rank Level 2 covariance matrix, (c) calculation of change in deviance to evaluate the HLM2F model compared to HLM2, (d) construction of the distribution of a set of random effects in terms of an underlying latent factor, and (e) more efficient estimates for the variance component parameters. (Author).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10