Multilevel Modeling

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.

Software demonstrations will be provided in separate R, SAS, SPSS, and Stata break-out groups at the end of each day (you choose which to attend). While it is helpful to have some familiarity with one of these programs, this is not necessary. The lectures which constitute the majority of the workshop are software-independent

Our motivating goal is to provide an intense yet enjoyable 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.

In an effort to continually improve our instruction we obtain student evaluations with each course offering. Here is a sample of reviews from our 2016 Multilevel Modeling workshop:

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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Dan Bauer and Patrick Curran co-teach the workshop and alternate lecturing throughout the day. We provide approximately 35 hours of total lecture time as well as a bound copy of the course notes and the computer demonstrations (approximately 600 pages total). Although there is not a hands-on computer lab component to this workshop, we provide extensive live software demonstrations and distribute the data and code for all examples. Further, participants are welcome to bring personal laptop computers to follow along with the software demonstrations or to work on their own data applications.

Please see sample copies of the lecture notes and SAS software notes (parallel versions exist in R, SPSS, and Stata).

All participants also have the opportunity to sign up for individual consulting meetings on Wednesday, Thursday, or Friday.

A continuous beverage service and afternoon snacks are provided each day. Participants are also welcome to enjoy the breakfast buffet at the Hampton Inn before class begins each morning.

Learn more about what makes our training unique...

We begin each day at 9:00 and continue until 12:15 with a mid-morning break. Lunch is from 12:15 to 1:30 and attendees can select from a large number of restaurants in the downtown area. The afternoon session continues from 1:30 to 3:30 and includes a mid-afternoon break. On Monday through Thursday, three break-out sessions are held from 3:30 to 5:00 that focus on fitting models in either R, SPSS, SAS, or Stata. The day concludes at 5:00 except on the final day which concludes at approximately 3:30 to allow for afternoon travel. Finally, we will host a happy hour on Monday at 5:00 with appetizers and drinks

We are very pleased to hold our workshop in the grand meeting room of the Chapel Hill-Carrboro Hampton Inn & Suites located at 370 East Main Street in Carrboro, NC. The Hampton Inn is set in the heart of Chapel Hill-Carrboro and is within walking distance of the campus of the University of North Carolina as well as to many dining, entertainment, and leisure options throughout the downtown area. All participants have full access to the breakfast buffet as well as drinks throughout the day and a light afternoon snack. Complimentary day time parking is provided at the parking deck immediately behind the Hampton Inn for workshop participants.

We have reserved a block of rooms in the Hampton Inn at a reduced rate that will be available until four weeks prior to the workshop or until the block is sold out. The hotel offers a wide range of amenities including workout facilities, a swimming pool, coin operated laundry, and much more. Rooms can be booked through online reservations or via phone at 919.969.6989 (refer to MLM group code).

There are also many additional local hotels available, some within walking distance and others which may offer a shuttle service to the downtown.

We offer a 10% reduction in total tuition for group enrollments (code GROUP) and for individuals enrolling in two or more workshops (code MULTI).

We also offer a fixed number of reduced-price registrations for graduate students who are actively enrolled in a recognized masters 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.

We are fortunate to have the opportunity to work in collaboration with the Society of Multivariate Experimental Psychology (SMEP) to provide a limited number of financial awards to students from under-represented groups to attend methodological workshops. These awards are made to qualifying students and post doctoral fellows with available funds of up to $1000 per student. Please see Support for Students from Underrepresented Groups to Attend Methodological Workshops for full details on both of these sources of support.

Curran-Bauer Analytics will fully refund registration fees for cancellations made with two weeks or more notice prior to the event. Registration fees are non-refundable if a cancellation is made less than two weeks before the event.

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 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 Example: Effects of Centering
3.4 Frequently Asked Questions

Chapter 4. Random Slopes and Cross-Level Interactions
4.1 Including Random Slopes for Level-1 Predictors
4.2 Example: Random Slopes
4.3 Modeling Cross-Level Interactions
4.4 Example: Cross-Level Interaction

Chapter 5. Model Assumptions and Model Evaluation
5.1 Model Assumptions
5.2 Model Assessment and Diagnostics
5.3 Maximum Likelihood Estimation

Chapter 6. The Analysis of Repeated Measures: Part 1
6.1 Growth Curves Within the Multilevel Framework
6.2 Alternative Metrics of Time
6.3 Alternative Level-1 Error Structures
6.4 Modeling Non-Linear Change

Chapter 7. The Analysis of Repeated Measures: Part 2
7.1 Time-Invariant Predictors of Growth
7.2 Multiple Groups Models
7.3 Time-Varying Covariates

Chapter 8. Multilevel Models for Intensive Longitudinal Data
8.1 Unique Features of Intensive Longitudinal Data
8.2 Modeling Time Trends
8.3 Serially Correlated Residuals
8.4 Within- and Between-Person Effect Decomposition

Chapter 9. The Analysis of Three-Level Data
9.1 Three-Level Models for Hierarchical Data
9.2 Three-Level Models for Longitudinal Data

Chapter 10. Multilevel Models for Discrete Outcomes
10.1 Discrete Dependent Variables
10.2 Generalized Linear Models
10.3 Multilevel Generalized Linear Models
10.4 Additional Considerations

Appendix A. Multilevel Mediation

Appendix B. Multivariate Multilevel Models

Appendix C. Cross-Classified Models