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.
Webinar Date & Time: August 19, 2026 at 3pm US ET.
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.
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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The linear model normality assumption, along with constant variance assumption, is quite robust to departures. That means that even if the 
assumptions aren’t met perfectly, the resulting p-values and confidence intervals will still be reasonable estimates.
This is great because it gives you a bit of leeway to run linear models, which are intuitive and (relatively) straightforward. This is true for both linear regression and ANOVA.
You do need to check the assumptions anyway, though. You can’t just claim robustness and not check. Why? Because some departures are so far off that the p-values and confidence intervals become inaccurate. And in many cases there are remedial measures you can take to turn non-normal residuals into normal ones.
But sometimes you can’t.
Sometimes it’s because the dependent variable just isn’t appropriate for a linear model. The (more…)
There are not a lot of statistical methods designed just to analyze ordinal variables.
But that doesn’t mean that you’re stuck with few options. There are more than you’d think.
Some are better than others, but it depends on the situation and research questions.
Here are five options when your dependent variable is ordinal.
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Multinomial logistic regression is an important type of categorical data analysis. Specifically, it’s used when your response variable is nominal: more than two categories and not ordered.
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When your dependent variable is not continuous, unbounded, and measured on an interval or ratio scale, linear models don’t fit. The data just will not meet the assumptions of linear models. But there’s good news, other models exist for many types of dependent variables.
Today I’m going to go into more detail about 6 common types of dependent variables that are either discrete, bounded, or measured on a nominal or ordinal scale and the tests that work for them instead. Some are all of these.
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Interpreting the results of logistic regression can be tricky, even for people who are familiar with performing different kinds of statistical analyses. How do we then share these results with non-researchers in a way that makes sense?
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