A Gentle Introduction to Random Slopes in Multilevel Modeling
…aka, how to look at cool interaction effects for nested data.
Do the words “random slopes model” or “random coefficients model” send shivers down your spine? These words don’t have to be so ominous. Journal editors are increasingly asking researchers to analyze their data using this particular approach, and for good reason.
In this Stat’s Amore training, Marc Diener will help you make sense of the concepts and data that you find in studies that use random slopes: multi-level models (MLM). You’ll learn the ideas behind random slope models, when to use this type of model, and how to go about adding variables into your model. A running example will be used to clarify the ideas and the meaning of the multilevel model results.
About the Instructor
Marc Diener, PhD is an Associate Professor in the Clinical Psychology Doctoral Program at Long Island University—Post, and he maintains an independent clinical and research/statistics consulting practice. He has published widely, including peer-reviewed journal articles, book chapters, and other publications. He serves as a consulting editor for several journals, and his professional presentations include peer-reviewed and invited talks. He is a Fellow in the Division of Independent Practice of the American Psychological Association. In his practice, he provides psychological testing, individual psychotherapy, clinical supervision/consultation, and research/statistics consultation.
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.