Structural equation models with latent variables
This talk will introduce the main ideas of structural equation models (SEMs) with latent variables and Stata tools that can be used for such models. The two approaches most often used in the applied work are numeric integration of the latent variables and covariance structure modeling. The first approach is implemented in Stata via -gllamm- (developed by Sophia Rabe-Hesketh). The second approach is currently implemented in -confa- for confirmatory factor analysis models. Also, introduction of the generalized method of moments (GMM) estimation and testing framework in version 11 of Stata made it possible to estimate SEMs by using moderately complex parameter and matrix manipulations. Working examples will be provided with some popular data sets (Holzinger-Swineford factor analysis model and Bollen's industrialization and political democracy model).
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