Member Training: Statistical Contrasts

Statistical contrasts are a tool for testing specific hypotheses and model effects, particularly comparing specific group means.

They allow you to answer questions that go beyond the standard model output, like comparing the outcome of two treatments when neither of them is the reference category. And determining whether there is an increasing or decreasing trend in the outcome as we move across the categories of an ordered categorical variable.

In this training, we explore the principles behind different types of statistical contrasts for factorial ANOVA and general linear models, how to set them up, and when we might choose to use them.

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

Kim is a workshop instructor and Statistically Speaking mentor for The Analysis Factor and owner/lead consultant at K.R. Love Quantitative Consulting and Collaboration.

She has worked as a statistical consultant and collaborator in multiple professional roles, most recently as the associate director of the University of Georgia Statistical Consulting Center.

Kim has more than a decade of professional and academic experience in the fields of regression and linear models, categorical data, generalized linear models, mixed effects models, nonlinear models, repeated measures, and experimental design. She has a B.A. in mathematics from the University of Virginia, and an M.S. and PhD in statistics from Virginia Tech.

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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.