Mplus – where latent ideas matter

About 30 years or so ago a modeling framework known as Structural Equation Models(SEM) became known for its ability to generalize and model observable and unobservable(latent) variables and to include specifications about multiple sources of variation. Early on Mplus was known for its treatment of models involving latent variables. Over the years Mplus has broaden its scope of features to more fully address mixed variable settings, advance simulation options, censoring/survival models, nonlinear growth and MultiLevel models. Some of the functionality in Mplus can be found in Spss’s Amos product and SAS’s Proc Calis feature, for example. Complex models such as these present unique chanllanges to the inference process. Both Mplus and Amos address this issue with optional bootstrapping of either residuals or observations. Whereas, R ‘s SEM package will bootstrap observations to estimate parameter standard errors. Additional information can be found at: http://www.statmodel.com/features.shtml

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