Multilevel Models for Hierarchical Data

Instructors: Dan Bauer & Patrick Curran
Software: R, SPSS, SAS, and Stata
Lectures: 12 hours
Software Demonstrations: 3-4 hours per program
Evergreen Content: Continually updated
Lifetime Access: Materials never expire

Class Overview Video

Professional: $779
Student: $599

Savings Opportunity: Get more content and save up to $350 with our Multilevel Modeling Bundle

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Category: Asynchronous, Workshops

Multilevel Models for Hierarchical Data focuses 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: Get lifetime access to both this class and Multilevel Models for Longitudinal Data at a significant savings of $350 off the professional rate (or $250 off the student rate) relative to the total cost when purchased separately through our Multilevel Modeling Bundle. This gives you a total of 28 hours of pre-recorded lectures plus many more hours of pre-recorded demonstrations in R, SAS, SPSS, and Stata.

1:42 Class Content / 7:39 Materials Provided / 9:26 Learning Objectives

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

Goals of the Workshop

Our motivating goal is to provide an interactive and engaging instructional experience that focuses on a large number of topics in multilevel modeling. We strive to strike an equal balance between core concepts of the underlying statistical model along with the practical application and interpretation of multilevel models fitted to real empirical data with a hierarchical structure. Our workshop is designed to provide participants with the materials and instruction needed to both develop a real understanding of the multilevel model and to be able to thoughtfully apply a variety of basic and advanced multilevel models to their own data.

Who Should Attend

Our workshop is designed for graduate students, post-doctoral fellows, faculty, and research scientists from the behavioral, social, and health sciences.

Although it is recommended that participants have a working knowledge of the multiple regression model, core concepts will be reviewed at the beginning of the workshop. Participants who would like a more thorough refresher on linear regression prior to attending may wish to watch our (no cost) Linear Regression Playlist on YouTube.

Syllabus

Chapter 1. General Introduction
1.1 Nested Data Structures
1.2 Why Conventional Statistics are Inappropriate for Nested Data
1.3 Methods for Analyzing Nested Data

Chapter 2. Basic Multilevel Models
2.1 The Random-Effects ANOVA Model
2.2 Incorporating Lower-Level Predictors
2.3 Incorporating Upper-Level Predictors

Chapter 3. Decomposing Within- and Between-Group Effects
3.1 Conceptualizing Total, Between-Group and Within-Group Effects
3.2 Centering Level 1 and Level 2 Predictors
3.3 Estimating and Testing Within- and Between-Group Effects

Chapter 4. Random Slopes and Cross-Level Interactions
4.1 Including Random Slopes for Level-1 Predictors
4.2 Modeling Cross-Level Interactions
4.3 Plotting and Probing Cross-Level Interactions

Chapter 5. Extensions and Next Steps
5.1 Summary of Core Topics Covered
5.2 Where to Go From Here: Extensions and Next Steps

Software Considerations

What is Provided

Participants receive lifetime access to all course materials, including complete course notes, lecture recordings, software demonstration notes, software demonstration recordings, and data and code for all examples. You can revisit these materials any time you like, without worrying about expiration dates. Pretty awesome, huh?

Content for this class is evergreen, meaning participants will always have access to the most current version of the course materials. Thus, if we replace a lecture recording or software demonstration, or update a syntax file, you will have immediate access to the updated content without needing to re-enroll in the class. Even more awesome.

Availability of Recordings

Certificate of Completion

After enrollment, you will have the option to generate a certificate of completion which will display your first and last name (as indicated in your user account), the name of the workshop, and the date upon which the certificate was generated. It can be printed to a PDF and will also be stored in the your user account for future access (after logging in to your account, select My Certificates from the user menu). Heck, you can even print out a hard copy and frame it to impress your friends.

Sample Content

Tuition Support for Students

We offer reduced-price registrations for undergraduate and graduate students who are actively enrolled in a recognized bachelor's, master's or doctoral training program. No application is necessary to qualify for the student tuition rates; simply choose the student rate when beginning the registration process at the top of the page. Confirmation of student status may be requested at a later time.

I’m a Ph.D. student, and this hierarchical MLM workshop has been one of the most thoughtfully structured learning experiences I’ve had. Dan and Patrick do an excellent job of walking through equations step by step, taking the time to explain what each piece means in a way that feels approachable and clear. They also chose excellent examples and visualizations that helped me gain a deeper understanding of what is happening at each step. The practical walkthroughs have also been immensely useful to go back to as I run different analyses in my work. In short, this course took me from not understanding hierarchical MLM to incorporating it into most of my research.

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.

Multilevel Models for Hierarchical Data

Instructors

Daniel J. Bauer, Ph.D.

Patrick J. Curran, Ph.D.

Workshop Details

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