High-Dimensional Exploratory Item Factor Analysis by a Metropolis-Hastings Robbins-Monro Algorithm

Autor/in	Cai, Li
Titel	High-Dimensional Exploratory Item Factor Analysis by a Metropolis-Hastings Robbins-Monro Algorithm
Quelle	In: Psychometrika, 75 (2010) 1, S.33-57 (25 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0033-3123
DOI	10.1007/s11336-009-9136-x
Schlagwörter	Quality of Life; Factor Structure; Factor Analysis; Computation; Probability; Maximum Likelihood Statistics; Simulation; Children; Measures (Individuals) + Suchen Sie Ihr Suchwort? Lebensqualität; Faktorenstruktur; Faktorenanalyse; Wahrscheinlichkeitsrechnung; Wahrscheinlichkeitstheorie; Simulation program; Simulationsprogramm; Child; Kind; Kinder; Messdaten
Abstract	A Metropolis-Hastings Robbins-Monro (MH-RM) algorithm for high-dimensional maximum marginal likelihood exploratory item factor analysis is proposed. The sequence of estimates from the MH-RM algorithm converges with probability one to the maximum likelihood solution. Details on the computer implementation of this algorithm are provided. The accuracy of the proposed algorithm is demonstrated with simulations. As an illustration, the proposed algorithm is applied to explore the factor structure underlying a new quality of life scale for children. It is shown that when the dimensionality is high, MH-RM has advantages over existing methods such as numerical quadrature based EM algorithm. Extensions of the algorithm to other modeling frameworks are discussed. (Contains 7 tables and 1 figure.) (As Provided).
Anmerkungen	Springer. 233 Spring Street, New York, NY 10013. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-348-4505; e-mail: service-ny@springer.com; Web site: http://www.springerlink.com
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10