A Class of Population Covariance Matrices in the Bootstrap Approach to Covariance Structure Analysis

Autor/inn/en	Yuan, Ke-Hai; Hayashi, Kentaro; Yanagihara, Hirokazu
Titel	A Class of Population Covariance Matrices in the Bootstrap Approach to Covariance Structure Analysis
Quelle	In: Multivariate Behavioral Research, 42 (2007) 2, S.261-281 (21 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0027-3171
DOI	10.1080/00273170701360662
Schlagwörter	Multivariate Analysis; Statistical Analysis; Statistical Inference; Matrices + Suchen Sie Ihr Suchwort? Multivariate Analyse; Statistische Analyse; Inferential statistics; Schließende Statistik; Matrizenrechnung
Abstract	Model evaluation in covariance structure analysis is critical before the results can be trusted. Due to finite sample sizes and unknown distributions of real data, existing conclusions regarding a particular statistic may not be applicable in practice. The bootstrap procedure automatically takes care of the unknown distribution and, for a given sample size, also provides more accurate results than those based on standard asymptotics. But the procedure needs a matrix to play the role of the population covariance matrix. The closer the matrix is to the true population covariance matrix, the more valid the bootstrap inference is. The current paper proposes a class of covariance matrices by combining theory and data. Thus, a proper matrix from this class is closer to the true population covariance matrix than those constructed by any existing methods. Each of the covariance matrices is easy to generate and also satisfies several desired properties. An example with nine cognitive variables and a confirmatory factor model illustrates the details for creating population covariance matrices with different misspecifications. When evaluating the substantive model, bootstrap or simulation procedures based on these matrices will lead to more accurate conclusion than that based on artificial covariance matrices. (Author).
Anmerkungen	Lawrence Erlbaum. Available from: Taylor & Francis, Ltd. 325 Chestnut Street Suite 800, Philadelphia, PA 19106. Tel: 800-354-1420; Fax: 215-625-2940; Web site: http://www.tandf.co.uk/journals/default.html
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10