Multilevel Modeling

June 5 to June 9, 2017
Chapel Hill, North Carolina
Instructors: Dan Bauer and Patrick Curran
Software Demonstrations: SAS, SPSS and Stata

Students in class

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. 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 with topics ranging from introductory to advanced, as described below.

Who Should Attend and Software Considerations

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 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.

The Goals of the Workshop

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.

Reviews From Past Participants

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.

Read more…

What is Provided

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 SPSS and Stata). A subset of examples are also coded in HLM6 and Mplus.

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

A continental breakfast and continuous snack and beverage service are also provided each day.

Learn more about what makes our training unique…

Daily Schedule

Each day begins at 8:30 with a free continental-style breakfast provided at the conference center. The morning session is from 9:00 to 12:15 and includes a mid-morning break. Lunch is from 12:15 to 1:30 and attendees can select from a large number of restaurants in downtown Chapel Hill. The afternoon session continues from 1:30 to 3:30 and includes a mid-afternoon break. Finally, on Monday through Thursday, three break-out sessions are held from 3:30 to 5:00 that are focused on the demonstration of fitting models in SAS, SPSS, 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 at The Back Bar which is located on the same floor as the lecture hall.

Facilities and Accommodations

We are very pleased to hold our workshop in the Great Room located at the Top of the Hill Restaurant and (yes) Brewery. The Great Room is located at 100 East Franklin Street at the historic intersection of Franklin Street and Columbia Street in downtown Chapel Hill and is just steps from the campus of the University of North Carolina. The Great Room is housed in the space once occupied by the legendary Carolina Theater and is characterized by soaring ceilings, hard wood floors, red brick walls, and flowing natural light.

We have reserved a block of reduced-rate rooms at the Hampton Inn for workshop participants that will be available until May 4th or the block is sold out (if you prefer to make a reservation by phone, please indicate you are with the Curran-Bauer Analytics group). Set in the heart of Chapel Hill/Carrboro, the Hampton Inn is less than one mile from the Great Room and it is approximately a 15 minute walk from the hotel to the meeting room. Chapel Hill also offers free buses and the J-line bus route stops right in front of the hotel and then at Franklin and Columbia, the intersection nearest the Great Room.

There are also many additional local hotels available, some within walking distance and others which offer free shuttle service to and from the The Great Room.

Tuition and Registration

Tuition for the five-day workshop is $1795 per registrant. We offer a fixed number of reduced-price registrations of $1295 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 select “student” when beginning the registration process. Confirmation of student status may be requested at a later time. We will maintain a waitlist once the student seats are filled.

We also offer a 10% reduction in total tuition when enrolling in two or more workshops. Simply proceed through the registration process and enter the code multi when prompted.

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Support for Junior Scholars from Under-Represented Groups

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.

Cancellation Policy

Curran-Bauer Analytics will refund registration fees for cancellations made with two weeks or more notice prior to the event. For credit card registrations, 10% will be deducted from the refund to pay transaction fees imposed by the credit card companies; there is an industry-imposed 4.95% charge to book the registration and another 4.95% to cancel the registration. For check or purchase order registrations, registration fees will be refunded in full. 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 Random Intercept Regression Models
2.3 Means/Intercepts as Outcomes

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

Chapter 4. Random-Slopes Regression and Slopes-as-Outcomes
4.1 Random-Slopes Regression
4.2 The General-Random-Effects Regression Model
4.3 Slopes-as-Outcomes

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

Please contact us either via email or by phone (919.533.9817) if you need any additional information or have any further questions.