dummy coding

Member Training: Dummy and Effect Coding

August 1st, 2014 by

Why does ANOVA give main effects in the presence of interactions, but Regression gives marginal effects?Stage 2

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.

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.

How to Interpret Odd Ratios when a Categorical Predictor Variable has More than Two Levels

July 1st, 2013 by

One great thing about logistic regression, at least for those of us who are trying to learn how to use it, is that the predictor variables work exactly the same way as they do in linear regression.

Dummy coding, interactions, quadratic terms–they all work the same way.

Dummy Coding

In pretty much every regression procedure in every stat software, the default way to code categorical variables is with dummy coding.

All dummy coding means is recoding the original categorical variable into a set of binary variables that have values of one and zero.  You may find it helpful to (more…)

Strategies for Choosing the Reference Category in Dummy Coding

March 18th, 2013 by

Every statistical software procedure that dummy codes predictor variables uses a default for choosing the reference category.

This default is usually the category that comes first or last alphabetically.

That may or may not be the best category to use, but fortunately you’re not stuck with the defaults.

So if you do choose, which one should you choose? (more…)

How to Combine Complicated Models with Tricky Effects

July 22nd, 2011 by

Need to dummy code in a Cox regression model?

Interpret interactions in a logistic regression?

Add a quadratic term to a multilevel model?

quadratic interaction plotThis is where statistical analysis starts to feel really hard. You’re combining two difficult issues into one.

You’re dealing with both a complicated modeling technique at Stage 3 (survival analysis, logistic regression, multilevel modeling) and tricky effects in the model (dummy coding, interactions, and quadratic terms).

The only way to figure it all out in a situation like that is to break it down into parts.  (more…)

Dummy Code Software Defaults Mess With All of Us

July 15th, 2011 by

In my last blog post, I wrote about a mistake I once made when I didn’t realize the defaults for dummy coding were different in two SPSS procedures (Binary Logistic and GEE).

Ironically, about the same time I wrote it, I was having a conversation with Ann Maria de Mars on Twitter.  She was trying to figure out why her logistic regression model fit results were identical in SAS Proc Logistic and SPSS Binary Logistic, but the coefficients in SAS were half those of SPSS.

It was ironic because I, of course, didn’t recognize it as the same issue and wasn’t much help.

But Ann Maria investigated and discovered that it came down to differences in the defaults for coding categorical predictors in SAS and SPSS that did it.  Her detailed and humorous explanation is here.

Some takeaways for you, the researcher and data analyst:

1. Give yourself a break if you hit a snag.  Even very experienced data analysts, statisticians who understand what they’re doing, get stumped sometimes.  Don’t ever think that performing data analysis is an IQ test.  You’re bringing together many skills and complex tools.

2. Learn thy software.  In my last post, I phrased it “Know thy software”, but this is where you get to know it.  Snags are good opportunities to investigate the details of your software, just like Ann Maria did.  If you can think of it as a challenge to figure out–a puzzle–it can actually be fun.

Make friends with your syntax manuals.

3. Get help when you need it. Statistical software packages *are* complex tools. You don’t have to know everything to use them

Ask colleagues.  Call customer support. Call a stat consultant.  That’s what they’re there for.

4. A great way to check your work is to run your test two different ways.  It’s another reason to be able to use at least two stat software packages.  I’m not suggesting you have to run every analysis twice.  But when a result looks strange, or you want to double-check a specific important model, this can be a good strategy for testing things out.

It may be that your results aren’t telling you what you think they are.

Bookmark and Share


When Dummy Codes are Backwards, Your Stat Software may be Messing With You

July 8th, 2011 by

One of the tricky parts about dummy coded (0/1) variables is keeping track of what’s a 0 and what’s a 1.

This is made particularly tricky because sometimes your software switches them on you.

Here’s one example in a question I received recently.  The context was a Linear Mixed Model, but this can happen in other procedures as well.

I dummy code my categorical variables “0” or “1” but for some reason in the (more…)