Stata has a rich set of operators for specifying factor variables in linear and nonlinear regression models. I will show how to test for the effects of factor variables in these models. I will also show how to compare and contrast these effects using linear combinations of the model coefficients.

Introducing generalized SEM: (1) SEM with generalized linear response variables, and (2) SEM with multilevel mixed effects, whether linear or generalized linear. Generalized linear response variables mean you can now fit probit, logit, Poisson, multinomial logistic, ordered logit, ordered...

In this presentation, I cover how to use Stata for survey data analysis assuming a fixed population. We will begin by reviewing the sampling methods used to collect survey data, and how they affect the estimation of totals, ratios, and regression coefficients. We will then cover the three...

In this presentation, I cover how to use Stata for survey data analysis assuming a fixed population. We will begin by reviewing the sampling methods used to collect survey data, and how they affect the estimation of totals, ratios, and regression coefficients. We will then cover the three...

In this presentation, I cover how to use the new factor variables features in Stata 11. Stata’s new factor variables notation allows you to identify categorical covariates as factor variables, provides a convenient notation for specifying indicator variables without having to generate them,...

This talk discusses Stata's features for analyzing survey data and correlated data, and will explain how and when to use the three major variance estimators for survey and correlated data: the linearization estimator, balanced repeated replications, and the clustered jackknife (the latter two...