ERIC Number: EJ749616
Record Type: Journal
Publication Date: 2007
Pages: 29
Abstractor: Author
ISBN: N/A
ISSN: ISSN-1070-5511
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The Effect of the Number of Observations per Parameter in Misspecified Confirmatory Factor Analytic Models
Jackson, Dennis L.
Structural Equation Modeling: A Multidisciplinary Journal, v14 n1 p48-76 2007
Some authors have suggested that sample size in covariance structure modeling should be considered in the context of how many parameters are to be estimated (e.g., Kline, 2005). Previous research has examined the effect of varying sample size relative to the number of parameters being estimated (N:q). Although some support has been found for this effect, the effect size appears to be small compared to other influences, such as indicator reliability and sample size (Jackson, 2003). Efforts to extend this work to the case where models are intentionally misspecified are described in this article. In addition to varying the number of observations per estimated parameter, several other known influences on model fit were varied such as sample size, the degree of misspecification, number of variables per factor, and the communality of the measured variables. The results suggest that decreasing the number of parameters to be estimated while holding sample size constant can help detect misspecification errors, and some fit indexes were more sensitive to this manipulation than others. In general, the effects of N:q were small relative to other experimental effects.
Descriptors: Sample Size, Factor Analysis, Structural Equation Models, Goodness of Fit, Error of Measurement
Lawrence Erlbaum Associates, Inc. 10 Industrial Avenue, Mahwah, NJ 07430-2262. Tel: 800-926-6579; Tel: 201-258-2200; Fax: 201-236-0072; e-mail: journals@erlbaum.com; Web site: https://www.LEAonline.com
Publication Type: Journal Articles; Reports - Research
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
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