effect coding

Member Training: Dummy and Effect Coding

Why does ANOVA give main effects in the presence of interactions, but Regression gives marginal effects?

What are the advantages and disadvantages of dummy coding and effect coding? When does it make sense to use one or the other?

How does each one work, really?

In this webinar, we’re going to go step-by-step through a few examples of how dummy and effect coding each tell you different information about the effects of categorical variables, and therefore which one you want in each situation.

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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Interpreting Interactions Between Two Effect-Coded Categorical Predictors

by Karen Grace-Martin 13 Comments

I recently received this great question:

Question:

Hi Karen, ive purchased a lot of your material and read a lot of your pdf documents w.r.t. regression and interaction terms. Its, now, my general understanding that interaction for two or more categorical variables is best done with effects coding, and interactions cont v. categorical variables is usually handled via dummy coding. Further, i may mess this up a little but hopefully you’ll get my point and more importantly my question, i understand that

1) given a fitted line Y = b0 + b1 x1 + b2 x2 + b3 x1*x2, the interpretation for b3 is the diff of the effect of x1 on Y, when x2 changes one unit, if x1 and x2 are cont. ( also interpretation can be reversed in terms of x1 and x2). [Read more…] about Interpreting Interactions Between Two Effect-Coded Categorical Predictors

Why ANOVA and Linear Regression are the Same Analysis

by Karen Grace-Martin 70 Comments

If your graduate statistical training was anything like mine, you learned ANOVA in one class and Linear Regression in another. My professors would often say things like “ANOVA is just a special case of Regression,” but give vague answers when pressed.

It was not until I started consulting that I realized how closely related ANOVA and regression are. They’re not only related, they’re the same thing. Not a quarter and a nickel–different sides of the same coin.

So here is a very simple example that shows why. When someone showed me this, a light bulb went on, even though I already knew both ANOVA and multiple linear [Read more…] about Why ANOVA and Linear Regression are the Same Analysis

Concepts in Linear Regression you need to know before learning Multilevel Models

by Karen Grace-Martin 2 Comments

It seems very many researchers are needing to learn multilevel and mixed models, and I have to say, it’s not so easy on your own.

I too went to graduate school before it was taught in classes–we did learn mixed models as in Split Plot designs, but things have progressed a bit since then. So I too have had to learn them without benefit of a class, or teacher. So I feel your pain. But I’ve struggled through and learned a [Read more…] about Concepts in Linear Regression you need to know before learning Multilevel Models