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Autor/inn/enZhang, Guangjian; Browne, Michael W.
TitelBootstrap Standard Error Estimates in Dynamic Factor Analysis
QuelleIn: Multivariate Behavioral Research, 45 (2010) 3, S.453-482 (30 Seiten)
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Spracheenglisch
Dokumenttypgedruckt; online; Zeitschriftenaufsatz
ISSN0027-3171
SchlagwörterStatistical Inference; Error of Measurement; Factor Analysis; Simulation; Measurement Techniques; College Freshmen; Females; Pregnancy; Psychological Patterns; Personality
AbstractDynamic factor analysis summarizes changes in scores on a battery of manifest variables over repeated measurements in terms of a time series in a substantially smaller number of latent factors. Algebraic formulae for standard errors of parameter estimates are more difficult to obtain than in the usual intersubject factor analysis because of the interdependence of successive observations. Bootstrap methods can fill this need, however. The standard bootstrap of individual timepoints is not appropriate because it destroys their order in time and consequently gives incorrect standard error estimates. Two bootstrap procedures that are appropriate for dynamic factor analysis are described. The moving block bootstrap breaks down the original time series into blocks and draws samples of blocks instead of individual timepoints. A parametric bootstrap is essentially a Monte Carlo study in which the population parameters are taken to be estimates obtained from the available sample. These bootstrap procedures are demonstrated using 103 days of affective mood self-ratings from a pregnant woman, 90 days of personality self-ratings from a psychology freshman, and a simulation study. (Contains 8 tables, 3 figures and 8 footnotes.) (As Provided).
AnmerkungenPsychology Press. 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
Erfasst vonERIC (Education Resources Information Center), Washington, DC
Update2017/4/10
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