OptinMon

Estimated marginal means (EMMs)—sometimes called least-squares means—are a powerful way to interpret and visualize results from linear and mixed-effects models. Yet many researchers struggle to extract, understand, and plot them.

In this 60-minute hands-on tutorial, participants will learn how to compute, interpret, and visualize EMMs using only base R functions together with the emmeans, car, and lme4 packages. We will start with simple linear models and progress to mixed models with random effects, highlighting how to obtain EMMs, confidence intervals, pairwise contrasts, and publication-ready base R plots. The session emphasizes conceptual understanding and practical code you can adapt immediately to your own analyses.

Note: This training is an exclusive benefit to members of the Statistically Speaking Membership Program and part of the Stat’s Amore Trainings Series. Each Stat’s Amore Training is approximately 90 minutes long.

About the Instructor

Manolo Romero Escobar is a seasoned statistical consultant and psychometrician with a passion for helping researchers.

Throughout his career, Manolo has worked extensively as a research and statistical consultant. He has served a diverse range of clients including health researchers, educational institutions, and government agencies. With a focus on linear mixed effects modeling, latent variable modeling, and scale development, Manolo brings a wealth of knowledge and experience to every project he undertakes.

Manolo is also proficient in statistical programming languages such as R, SPSS, and Mplus, and has experience with Python and SQL. He is passionate about leveraging technology as an educational and training tool, and he continuously enhances his skills to stay at the forefront of his field.

He holds a B.A. and Licentiate degree in Psychology from Universidad del Valle de Guatemala and a M.A. in Psychology (Area: Developmental and Cognitive Processes) from York University.

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From the last posts in this series, you should feel comfortable using Stata’s data editor, changing values and types, and creating new variables.  

We’ll now teach you to make your variables more approachable by adding labels. 

The image below shows label information for the foreign variable.  

(more…)

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Once you’ve imported, examined, and cleaned your data, a common next step would be to make some visual displays or graphs. In this article we’ll go over the details of creating, naming, saving, and exporting graphs in Stata.

We will do all of this using syntax, rather than Stata’s “Graphics” menu. If you want a quick lesson on using the menus to make graphs in Stata, check out this article. (more…)

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There are not a lot of statistical methods designed just for ordinal variables. (There are a few, though.)  

But that doesn’t mean that you’re stuck with few options.  There are more than you’d think. (more…)

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A normal probability plot is extremely useful for checking normality assumptions.  It’s more precise than a histogram, which can’t pick up subtle deviations. And yet it doesn’t suffer from too much power from large samples with tiny departures from normality or too little power from small samples with large departures from normality, as do tests like Shaprio-Wilkes.

The biggest problem with a normal probability plot is that it’s hard to read, especially if you’re not used to them. So let’s take a moment and walk through exactly how they work and what they tell you.

There are two versions of normal probability plot: Q-Q and P-P.  I’ll start with the Q-Q.   (more…)

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From the last post in this series, you should know how to change between numeric types and easily change numeric data.  We’ll now expand your type-changing skills to include changing string variables with two new commands.   (more…)

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