You want a graph that most effectively communicates your message. You want a graph that fits the style of your journal. You want a graph with colors that everyone can differentiate. Or you want a graph in grayscale. Read more…

In his blog post, Enrique Pinzon discussed how to perform regression when we don’t want to make any assumptions about functional form—use the npregress command. He concluded by asking and answering a few questions about the results using the margins and marginsplot commands.

Recently, I have been thinking about all the different types of questions that we could answer using margins after nonparametric regression, or really after any type of regression. margins and marginsplot are powerful tools for exploring the results of a model and drawing many kinds of inferences. In this post, I will show you how to ask and answer very specific questions and how to explore the entire response surface based on the results of your nonparametric regression.
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Have you seen the latest issue of the Stata News? It has a new format that we think you will love. And, I want to make sure that you are not missing out on the articles discussing what our developers and users are doing with Stata.

We have a new section, User’s corner, that highlights interesting, useful, and fun user contributions. In this issue, you will see how Belén Chavez uses Stata to analyze her Fitbit® data.

We kept your favorite sections, including Spotlight articles written by Stata developers. In this issue, Enrique Pinzón demonstrates Estimating, graphing, and interpreting interactions using margins.

If you haven’t been reading the Stata News, you may want to browse previous Spotlight articles on topics such as endogenous treatment effects, item response theory (IRT), and Bayesian analysis. You can find all the previous Spotlight articles here.

When fitting almost any model, we may be interested in investigating whether parameters differ across groups such as time periods, age groups, gender, or school attended. In other words, we may wish to perform tests of moderation when the moderator variable is categorical. For regression models, this can be as simple as including group indicators in the model and interacting them with other predictors.

We naturally have hypotheses regarding differences in parameters across groups when fitting structural equation models as well. When these models involve latent variables and the corresponding observed measurements, we can test whether those measurements are invariant across groups. Evaluation of measurement invariance typically involves a series of tests for equality of measurement coefficients (factor loadings), equality of intercepts, and equality of error variances across groups.

In this post, I demonstrate how to use the sem command’s group() and ginvariant() options as well as the postestimation command estat ginvariant to easily perform tests of measurement invariance. Read more…