Stata Press is pleased to announce the release of Multilevel and Longitudinal Modeling Using Stata, Volumes I and II, Fourth Edition by Sophia Rabe-Hesketh and Anders Skrondal. This book debuted on the top 10 list for Kindle’s new releases for Probability & Statistics and consistently stayed there for weeks. This book was also on the top 10 list for Kindle’s new releases in Mathematics, competing with many other books. Read more…
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gsem is a very flexible command that allows us to fit very sophisticated models. However, it is also useful in situations that involve simple models.
For example, when we want to compare parameters among two or more models, we usually use suest, which combines the estimation results under one parameter vector and creates a simultaneous covariance matrix of the robust type. This covariance estimate is described in the Methods and formulas of [R] suest as the robust variance from a “stacked model”. Actually, gsem can estimate these kinds of “stacked models”, even if the estimation samples are not the same and eventually overlap. By using the option vce(robust), we can replicate the results from suest if the models are available for gsem. In addition, gsem allows us to combine results from some estimation commands that are not supported by suest, like models including random effects. Read more…
xtmixed was built from the ground up for dealing with multilevel random effects — that is its raison d’être. sem was built for multivariate outcomes, for handling latent variables, and for estimating structural equations (also called simultaneous systems or models with endogeneity). Can sem also handle multilevel random effects (REs)? Do we care?
This would be a short entry if either answer were “no”, so let’s get after the first question. Read more…