Dummy Coding

Your Questions Answered from the Interpreting Regression Coefficients Webinar

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

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:

Random Intercept and Random Slope Models

by Karen Grace-Martin 2 Comments

This free, one-hour webinar is part of our regular Craft of Statistical Analysis series. In it, we will introduce and demonstrate two of the core concepts of mixed modeling—the random intercept and the random slope.

Most scientific fields now recognize the extraordinary usefulness of mixed models, but they’re a tough nut to crack for someone who didn’t receive training in their methodology.

But it turns out that mixed models are actually an extension of linear models. If you have a good foundation in linear models, the extension to mixed models is more of a step than a leap. (Okay, a large step, but still).

You’ll learn what random intercepts and slopes mean, what they do, and how to decide if one or both are needed. It’s the first step in understanding mixed modeling.

***Note: This webinar has already taken place. Sign up below to get access to the video recording of the webinar.

Five Tips and Tricks: How to Make Stata Easier to Use

by Jeff Meyer 3 Comments

Stata allows you to describe, graph, manipulate and analyze your data in countless ways. But at times (many times) it can be very frustrating trying to create even the simplest results. Join us and learn how to reduce your future frustrations.

This one hour demonstration is for new and intermediate users of Stata. If you’re a beginner, the drop down commands can be extremely daunting.

If you’re an intermediate user and not constantly using Stata, it’s impossible to remember which commands generate the results you are looking to create.

This webinar, by guest presenter Jeff Meyer, will give you five actionable tips (and examples you can re-use) that will make your next analysis in Stata much simpler.

We’ll explore:

Save time with a do-file to create the table you want exactly as you want.
A few methods (some easier than others) to create dummy variables out of a categorical variable with several categories
At least three ways to insert a table into a document
Quickly alter the looks of your graphs through the use of macros
How to aggregate data to the group level based on a number of parameters

***Note: This webinar has already taken place. Sign up below to get access to the video recording of the webinar.

Statistically Speaking members can access this recording from the Analysis Toolbox Resources page at the Programs Center without signing up.

Our next free webinar is titled: “Random Intercept and Random Slope Models” and is coming up in August

Jeff Meyer is a statistical consultant with The Analysis Factor, a stats mentor for Statistically Speaking membership, and a workshop instructor. Read more about Jeff here.

Member Training: Dummy and Effect Coding

by Karen Grace-Martin 2 Comments

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.

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 and 85+ other stats trainings — plus the expert guidance you need to progress 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

by Karen Grace-Martin 4 Comments

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 [Read more…] about How to Interpret Odd Ratios when a Categorical Predictor Variable has More than Two Levels