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.
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, 130+ 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.
Recent innovations in data science raise troubling questions about discrimination and loss of privacy. 
In this training, you will see how statistics are not cold, hard numbers, but reflections of the perspectives and biases of those collecting the data.
We’ll discuss several case studies that show how real the harms can be. You’ll learn about some of these ethical problems and some possible solutions.
Webinar Date & Time: May 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.
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Mediation analysis is a model of the causal pathway between an independent variable, X, and a dependent variable, Y, through a mediating variable, M.
In this training, you’ll learn the right way to test mediation and the design issues that allow a causal interpretation.
We’ll discuss a number of issues in testing mediation, new and old. These include the Baron & Kenny 4-step approach, the difference between mediation and moderation, and Sobel and bootstrap tests for indirect effects.
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.
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, 130+ 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.
Regression models, such as linear, logistic, time to event, and mixed models, measure the strength of the association between the dependent variable and the independent variables.
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When you’re working with many correlated variables, they get too unwieldy to use individually.
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Cross-over trials provide a very powerful approach for comparing two treatment conditions. Research subjects get both treatment conditions, which we will label arbitrarily as A and B.
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