Multilevel Models for Hierarchical Data
December 11-13, 2023
10:00 am – 3:30 pm (Eastern US time)
Replay Access: 1 year from end of workshop
Instructors: Dan Bauer & Patrick Curran
Software Demonstrations: Pre-recorded in R, SPSS, SAS, and Stata
Multilevel Models for Hierarchical Data is a three-day workshop focused on the application and interpretation of multilevel models, also known as hierarchical linear models and mixed models, for the analysis of hierarchical data. Hierarchical data structures arises from sampling units at multiple levels, wherein lower-level units are nested within higher-level units. Common instances are students nested within schools, siblings nested within families, patients nested within therapists, and adults nested within communities. Analyzing such data using traditional general linear models (e.g., ANOVA or regression) is flawed, oftentimes substantially so. Not only are tests of significance likely biased, but many important within-group and between-group relations cannot be evaluated. All of these limitations can be addressed within the multilevel model. In this workshop we provide a comprehensive exploration of multilevel models for hierarchical data, with practical guidance on how to apply these in your own work.
Note: This workshop is a shorter version of our 5-day Multilevel Modeling workshop, which is currently available for self-paced access. Whereas the 3-day livestream this December focuses specifically on applications with hierarchical data structures, the longer class also includes coverage of applications to longitudinal data. Additionally, the longer class extends these applications to three-level models that blend hierarchical and longitudinal structures (e.g., repeated measures nested within persons nested within clusters). Those interested in these additional topics may prefer to enroll in the longer version of the class.
Daniel J. Bauer, Ph.D.
Dan Bauer is a Professor and the Director of the L.L. Thurstone Psychometric Laboratory in the Department of Psychology and Neuroscience at the University of North Carolina. He teaches primarily graduate-level courses in statistical methods, for which he has won multiple teaching awards. Read More
Patrick J. Curran, Ph.D.
Patrick Curran is a Professor in the L.L. Thurstone Psychometric Laboratory in the Department of Psychology and Neuroscience at the University of North Carolina at Chapel Hill. He is dedicated to teaching and disseminating advanced quantitative methods and has won multiple awards for teaching excellence. Read More
In an effort to continually improve our instruction we obtain student evaluations with each course offering. Here is a sample of reviews from prior Multilevel Modeling workshops:
Thank you for such an amazing workshop! I learned so much and cannot wait to dig into my dissertation data!
The concepts and practical applications are hard for me, but in this class I really felt like I’ve had some Eureka moments. It really came together for me. I think that speaks to both of your teaching abilities, the format of the class, and the organization of the materials. I love the dynamic between you two and your teaching styles complement each other well. I had a blast, a really good time, and loved it.
Everything is a strength -- balance between lecture/practical, the pace at which the material is delivered, the course material/resources, etc. Really just everything.
The workshop does a great job of explaining MLM on a conceptual level and provides useful examples to better understand the modeling. The instructors were great at teaching the material and were very open to answering questions.
This workshop strikes a perfect balance between theory and practical applications. I particularly appreciated this as my MLM grad class was several years ago, so this was a refresher and an extension now that I have the data to actually analyze.
December 14-16, 2023
Livestream & Replay Access
Instructor: Dan Bauer & Patrick Curran