Multilevel Modeling

Length: Five Days
Instructors: Dan Bauer & Patrick Curran
Software Demonstrations: R, SAS, SPSS, Stata
Lifetime Access: No expirations

Student: $990
Professional: $1290

Registration Type

Clear

Category: Asynchronous, Workshops

Multilevel Modeling is a five-day workshop focused on the application and interpretation of multilevel models, also known as hierarchical linear models and mixed models, for the analysis of nested data structures. Nesting can arise from hierarchical data structures (e.g., siblings nested within family; patients nested within therapist), longitudinal data structures (repeated measures nested within individual), or both (repeated measures nested within patient and patient nested within therapist). It is well known that the analysis of nested data structures using traditional general linear models (e.g., ANOVA or regression) is flawed, oftentimes substantially so. Indeed, tests of significance are likely biased and many important within-group and between-group relations cannot be evaluated. Not only can these limitations be addressed within the multilevel model, but this general framework provides methods for testing hypotheses in ways not previously possible. In this workshop we provide a comprehensive exploration of multilevel models with topics ranging from introductory to advanced.

Instructors

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

Workshop Details

Goals of the Workshop

Our motivating goal is to provide an interactive and engaging instructional experience that focuses on a large number of both introductory and advanced 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. 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 general regression model, these 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
2.4 Maximum Likelihood Estimation

Chapter 3. Decomposing Between- and Within-Group Effects
3.1 Total, Between-Group and Within-Group Effects
3.2 Centering Level 1 and Level 2 Predictors
3.3 Frequently Asked Questions

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. The Analysis of Repeated Measures: Part 1
5.1 Growth Curves Within the Multilevel Framework
5.2 Alternative Metrics of Time
5.3 Alternative Level-1 Error Structures
5.4 Modeling Non-Linear Change

Chapter 6. The Analysis of Repeated Measures: Part 2
6.1 Time-Invariant Predictors of Growth
6.2 Time-Varying Covariates

Chapter 7. The Analysis of Three-Level Data
7.1 Three-Level Models for Hierarchical Data
7.2 Three-Level Models for Longitudinal Data

Appendix A. Assumptions and Diagnostics
A.1 Model Assumptions
A.2 Model Assessment and Diagnostics

Appendix B. Multivariate Multilevel Models

Appendix C. Cross-Classified Models

Software Considerations

What is Provided

Multilevel Modeling is a five-day workshop originally co-taught live via Zoom by Dan Bauer and Patrick Curran. Daily lectures were held from 10:00 to 3:30 with morning, lunch, and afternoon breaks. Sessions consisted of comprehensive lectures, detailed presentation of real-data examples, response to participant questions, and general discussion. Pre-recorded software demonstration videos are also available in SPSS, SAS, Stata, and R.

Self-paced 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?

Availability of Recordings

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.

Reviews

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.

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Multilevel Modeling

Instructors

Daniel J. Bauer, Ph.D.

Patrick J. Curran, Ph.D.

Workshop Details

Reviews

Quick Navigation

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