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Interpreting Interactions

Centering a Covariate to Improve Interpretability

by Karen Grace-Martin  4 Comments

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

Tagged With: categorical predictor, centering, continuous predictor, Interpreting Interactions, parameter estimates, SPSS GLM

Related Posts

  • SPSS GLM or Regression? When to use each
  • Dummy Coding in SPSS GLM–More on Fixed Factors, Covariates, and Reference Groups, Part 1
  • SPSS GLM: Choosing Fixed Factors and Covariates
  • Interpreting Regression Coefficients

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

Tagged With: dummy coding, dummy variable, effect size, eta-square, Interpreting Interactions, interpreting regression coefficients, Reference Group, spotlight analysis, statistical significance

Related Posts

  • Using Marginal Means to Explain an Interaction to a Non-Statistical Audience
  • Interpreting Interactions in Linear Regression: When SPSS and Stata Disagree, Which is Right?
  • About Dummy Variables in SPSS Analysis
  • Interpreting (Even Tricky) Regression Coefficients – A Quiz

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

Tagged With: Interpreting Interactions, Interpreting Intercepts, interpreting regression coefficients, linear regression, marginal means

Related Posts

  • Your Questions Answered from the Interpreting Regression Coefficients Webinar
  • Understanding Interactions Between Categorical and Continuous Variables in Linear Regression
  • Clarifications on Interpreting Interactions in Regression
  • Interpreting Lower Order Coefficients When the Model Contains an Interaction

Understanding Interactions Between Categorical and Continuous Variables in Linear Regression

by Jeff Meyer  24 Comments

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

Tagged With: categorical variable, continuous variable, interaction, Interpreting Interactions, linear regression

Related Posts

  • Using Marginal Means to Explain an Interaction to a Non-Statistical Audience
  • Understanding Interaction Between Dummy Coded Categorical Variables in Linear Regression
  • Interpreting Lower Order Coefficients When the Model Contains an Interaction
  • When NOT to Center a Predictor Variable in 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:

[Read more…] about Interpreting Interactions in Linear Regression: When SPSS and Stata Disagree, Which is Right?

Tagged With: dummy coding, Interactions in Regression, Interpreting Interactions, interpreting regression coefficients, slopes

Related Posts

  • Your Questions Answered from the Interpreting Regression Coefficients Webinar
  • SPSS GLM or Regression? When to use each
  • Dummy Coding in SPSS GLM–More on Fixed Factors, Covariates, and Reference Groups, Part 1
  • Interpreting Lower Order Coefficients When the Model Contains an Interaction

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.

Not a Member? Join!

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.

Not a Member Yet?
It’s never too early to set yourself up for successful analysis with support and training from expert statisticians. Just head over and sign up for Statistically Speaking.

You'll get access to this training webinar, 100+ other stats trainings, a pathway to work through the trainings that you need — plus the expert guidance you need to build statistical skill with live Q&A sessions and an ask-a-mentor forum.

Tagged With: ANOVA, Interactions in Regression, Interpreting Interactions

Related Posts

  • Member Training: Interactions in ANOVA and Regression Models, Part 1
  • Member Training: Centering
  • Member Training: Elements of Experimental Design
  • Member Training: Hierarchical Regressions

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