Using an EM Covariance Matrix to Estimate Structural Equation Models With Missing Data: Choosing an Adjusted Sample Size to Improve the Accuracy of Inferences-Reference-Cited by-同舟云学术

Using an EM Covariance Matrix to Estimate Structural Equation Models With Missing Data: Choosing an Adjusted Sample Size to Improve the Accuracy of Inferences

Published:2004-01 Issue:1 Volume:11 Page:1-19
ISSN:1070-5511
Container-title:Structural Equation Modeling: A Multidisciplinary Journal
language:en
Short-container-title:Structural Equation Modeling: A Multidisciplinary Journal

Author:

Enders Craig K.,Peugh James L.

Publisher

Informa UK Limited

Subject

General Economics, Econometrics and Finance,Sociology and Political Science,Modelling and Simulation,General Decision Sciences

Link

http://www.tandfonline.com/doi/pdf/10.1207/S15328007SEM1101_1

Reference18 articles.

1. Arbuckle, J. L. (1996). Full information estimation in the presence of incomplete data. In G. A. Marcoulides & R. E. Schumacker (Eds.), Advanced structural equation modeling (pp. 243-277). Mahwah, NJ: Lawrence Erlbaum Associates, Inc.

2. Bollen, K. A. (1989). Structural equations with latent variables. New York: Wiley.

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