Interpreting Interactions

Centering a Covariate to Improve Interpretability

Centering a covariate –a continuous predictor variable–can make regression coefficients much more interpretable. That’s a big advantage, particularly when you have many coefficients to interpret. Or when you’ve included terms that are tricky to interpret, like interactions or quadratic terms.

For example, say you had one categorical predictor with 4 categories and one continuous covariate, plus an interaction between them.

First, you’ll notice that if you center your covariate at the mean, there is [Read more…] about Centering a Covariate to Improve Interpretability

Your Questions Answered from the Interpreting Regression Coefficients Webinar

by Karen Grace-Martin Leave a Comment

Last week I had the pleasure of teaching a webinar on Interpreting Regression Coefficients. We walked through the output of a somewhat tricky regression model—it included two dummy-coded categorical variables, a covariate, and a few interactions.

As always seems to happen, our audience asked an amazing number of great questions. (Seriously, I’ve had multiple guest instructors compliment me on our audience and their thoughtful questions.)

We had so many that although I spent about 40 minutes answering [Read more…] about Your Questions Answered from the Interpreting Regression Coefficients Webinar

Using Marginal Means to Explain an Interaction to a Non-Statistical Audience

by Jeff Meyer 8 Comments

Even with a few years of experience, interpreting the coefficients of interactions in a regression table can take some time to figure out. Trying to explain these coefficients to a group of non-statistically inclined people is a daunting task.

For example, say you are going to speak to a group of dieticians. They are interested [Read more…] about Using Marginal Means to Explain an Interaction to a Non-Statistical Audience

Understanding Interactions Between Categorical and Continuous Variables in Linear Regression

by Jeff Meyer 24 Comments

by Jeff Meyer, MPA, MBA

We’ve looked at the interaction effect between two categorical variables. Now let’s make things a little more interesting, shall we?

What if our predictors of interest, say, are a categorical and a continuous variable? How do we interpret the interaction between the two? [Read more…] about Understanding Interactions Between Categorical and Continuous Variables in Linear Regression

Interpreting Interactions in Linear Regression: When SPSS and Stata Disagree, Which is Right?

by Jeff Meyer Leave a Comment

Sometimes what is most tricky about understanding your regression output is knowing exactly what your software is presenting to you.

Here’s a great example of what looks like two completely different model results from SPSS and Stata that in reality, agree.

The Model

I ran a linear model regressing “physical composite score” on education and “mental composite score”.

The outcome variable, physical composite score, is a measurement of one’s physical well-being. The predictor “education” is categorical with four categories. The other predictor, mental composite score, is continuous and measures one’s mental well-being.

I am interested in determining whether the association between physical composite score and mental composite score is different among the four levels of education. To determine this I included an interaction between mental composite score and education.

The SPSS Regression Output

Here is the result of the regression using SPSS:

Member Training: Interactions in ANOVA and Regression Models, Part 2

by Karen Grace-Martin Leave a Comment

In this follow-up to December’s webinar, we’ll finish up our discussion of interactions.

There is something about interactions that is incredibly confusing.

An interaction between two predictor variables means that one predictor variable affects a third variable differently at different values of the other predictor.

How you understand that interaction depends on many things, including:

Whether one, or both, of the predictor variables is categorical or numerical
How each of those variables is coded (specifically, whether each categorical variable is dummy or effect coded and whether numerical variables are centered)
Whether it’s a two-way or three-way interaction
Whether there is a directionality to the interaction (moderation) or not

Sometimes you need to get pretty sophisticated in your coding, in the output you ask for, and in writing out regression equations.

In this webinar, we’ll examine how to put together and break apart output to understand what your interaction is telling you.

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.

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About the Instructor

Karen Grace-Martin helps statistics practitioners gain an intuitive understanding of how statistics is applied to real data in research studies.

She has guided and trained researchers through their statistical analysis for over 15 years as a statistical consultant at Cornell University and through The Analysis Factor. She has master’s degrees in both applied statistics and social psychology and is an expert in SPSS and SAS.

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