Structural equation modeling (SEM) is a widely used statistical method in the social and behavioral sciences. In its early days, SEM was restricted to linear models among latent variables. This thesis will illustrate the maximum likelihood method for estimating linear models, the product indicator, the two-step methods, and the mixture method for estimating non-linear models. All examples will be executed through the statistical software R. Additionally, examples of bootstrapping will be shown in the context of SEM for the purpose of comparing different estimation methods, performing power analysis, and determining model fit for small and large sample sizes.
|Commitee:||Safer, Alan, Suaray, Kagba|
|School:||California State University, Long Beach|
|Department:||Mathematics and Statistics|
|School Location:||United States -- California|
|Source:||MAI 55/02M(E), Masters Abstracts International|
|Keywords:||Equation, Estimation, Modeling, Sem, Structural|
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