September 2019 Member Training: Interpretation of Effect Size Statistics

Effect size statistics are required by most journals and committees these days ⁠— for good reason. 

They communicate just how big the effects are in your statistical results ⁠— something p-values can’t do.

But they’re only useful if you can choose the most appropriate one and if you can interpret it.

This can be hard in even simple statistical tests. But once you get into  complicated models, it’s a whole new story.

In this month’s Stat’s Amore training, member and guest instructor Marc Diener will make clear how to choose and interpret (in plain English!) a wide variety of standardized effect size statistics.

You’ll learn about special cases where standardized effect size statistics are difficult to calculate, like in multilevel models. He’ll help you understand empirically derived benchmarks for interpretation and the real level of usefulness of Cohen’s guidelines.

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.

Date and Time

Wednesday, September 25, 2019
12pm – 1:30pm (US EDT) (In a different time zone?)

About the Instructor

Marc Diener, PhD, maintains an independent clinical and research/statistics consulting practice and is an Associate Professor in the Clinical Psychology Doctoral Program at Long Island University—Post. In his practice, he provides psychological testing; individual psychotherapy; and clinical, research, and statistics consultation. He has published widely, including peer-reviewed journal articles and book chapters. He serves as a consulting editor for several journals, and his professional presentations include peer-reviewed and invited talks. He earned his doctorate in clinical psychology from Adelphi University and trained at Bellevue Hospital and St. Luke’s Roosevelt Hospital Center. He completed a postdoctoral fellowship at The Addiction Institute of New York/St. Luke’s Roosevelt Hospital Center. Prior to Long Island University—Post, he was a faculty member at the American School of Professional Psychology, Washington, DC. He is a Fellow in the Division of Independent Practice of the American Psychological Association.

Not a Member Yet?

It’s never too early (or late) 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 exclusive access to this training webinar, plus live Q&A sessions, a private stats forum, 75+ other stats trainings, and more.

Tagged as: Cohen's d, Correlation, correlation indexes, effect size, effect size statistics, empirically derived, Glass, Hedges, interpretation, interpreting, null hypothesis, probability of superiority, Proportion, strength association, superiority, variance