Multilevel Modeling: June 4 to June 8, 2012
Multilevel Modeling is a five-day workshop focused on the application and interpretation of statistical models that are designed 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 linear models with topics ranging from introductory to advanced. A detailed syllabus is presented below. The general structure of each day is lecture-based instruction from 9:00 to 3:30 (with morning, lunch, and afternoon breaks), and separate break-out sessions from 3:30 to 5:00 focused on model estimation using SAS and SPSS. Although there is not a hands-on computer lab component to this workshop, we provide extensive live demonstrations in both SAS and SPSS 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. A summary of participant reviews from prior workshops is available here.
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. Further, prior knowledge in either SAS or SPSS is useful but not required.
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.
What is Provided
We provide all participants with a bound set of detailed course notes along with a USB disk key that contains all data sets and computer code. A sample chapter of our course notes is located here and a sample chapter from the computer demonstration notes using SAS is located here; an alternative version is provided that presents the same examples using SPSS. Examples are also coded in HLM6 and Mplus. Finally, tuition includes a continental breakfast, a full lunch buffet, and drinks and snacks throughout the day.
Daily Schedule
Each day begins at 8:00 with a continental-style breakfast provided at the conference center. Instruction then begins at 9:00, there is a mid-morning break, and then concludes at 12:00 for a full buffet lunch provided to all participants. Lecture continues from 1:30 to 3:00, there is a mid-afternoon break, and then two break-out sessions each lead by Dr. Curran and Dr. Bauer are held from 3:30 to 5:00 focused on the demonstration of fitting models in either SAS or SPSS. The day concludes at 5:00 (except on the final day, which concludes at 3:00 to allow for afternoon travel).
Tuition and Registration
Tuition for the five-day workshop is $1800 per registrant; a $200 discount is offered such that enrolling in both SEM and MLM is $3400. The tuition includes approximately 35 hours of total lecture time, a bound copy of the course notes and a bound copy of the computer demonstrations (approximately 600 pages total), a USB key containing all data and programs, and a continental breakfast and full lunch each day. Registration is fully web-based and is accessed here.
Reduced Student Tuition and Minority Fellowship Travel Awards
We offer a limited number of reduced-tuition scholarships for students who are currently enrolled in a graduate degree-granting program. We also offer the opportunity to apply for a minority scholar travel award. Complete details are provided here.
Cancellation Policy
Curran-Bauer Analytics will refund registration fees for cancellations made on or before May 20, 2012, 5:00 PM (EST). 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 after May 20, 2012, 5:00 PM (EST).
Facilities and Accommodations
Our workshop will be held at the Rizzo Conference Center which provides a picturesque, retreat-like atmosphere with superb instructional facilities and is only minutes from historic downtown Chapel Hill. The Rizzo is owned by the Kenan-Flagler Business School at the University of North Carolina at Chapel Hill and is a full service conference center including hotel rooms, dining, exercise facilities, jogging trails, and a free local shuttle service. The Rizzo was ranked #1 for food and accommodations in the 2008 Executive Development survey conducted by the Financial Times.
We have reserved a block of rooms on-site at the Rizzo Center, and participants can make reservations at the special conference rate of $179 per night; there is a reduced rate of $152 per night for Friday and Saturday if you are staying for both workshops. This includes a full buffet breakfast and full buffet lunch, and the rooms are located in the same building as is the workshop. Reservations can be made in one of two ways. First, you can call 919.913.2098 and identify yourself as part of the multilevel modeling group with group code MLM1 to receive the special room rate. Second, you can register online here and follow the automated registration instructions. The group code will be automatically populated with “MLM1”; the boxes for Corporate/Promotional Code and IATA number are not needed and can be left blank.
If a participant chooses to not stay at the Rizzo Conference Center itself (or if the reserved block of rooms is filled), there are a number of alternative hotels that are conveniently located near the conference center. Note that all of these alternative accomodations require some form of transportation to the Rizzo Conference Center. A list of these hotels is located here.
Further Information
Please contact us either via email (cba at cbanalytics dot org) or by phone (919.533.9817) if you need additional information or have any further questions.
Syllabus
Day 1
•When to use multilevel models and why
•Common examples of nested data
•Dependence -- the problem with applying conventional statistics to nested data
•Approaches for analyzing nested data structures:
•Fixed-effects models – sometimes called econometric modeling approach
•Random-effects models – commonly referred to as multilevel modeling
•A Useful Starting Point: the Random Effects ANOVA model
•Partitioning variance into within- and between-groups components
•Computing and interpreting the intraclass correlation (ICC)
•Level 1 predictors: Random effects regression models
•Random intercepts; Random slopes
Day 2
•Level 2 predictors: Intercepts and Slopes as Outcomes
•Probing cross-level interactions
•Disentangling within- and between-group effects for Level 1 predictors by centering
•Group-mean centering; Grand-mean centering
•Avoiding errors of inference across levels (ecological fallacy, Simpson’s paradox)
•Estimators, assumptions and diagnostic procedures
•An overview of estimators for the model: ML and REML
•How to choose between estimators
•Accuracy and precision of estimates in small samples
•Flexibility for testing differences between models
Day 3
•Assumptions of the multilevel linear model
•Procedures for diagnosing and remedying violations of assumptions
•Modeling growth in longitudinal data
•Traditional modeling means with repeated measures ANOVA and MANOVA
•Modeling individual trajectories via multilevel models
•Alternative metrics of time
•Relaxing assumptions about the residuals (homoscedasticity, independence)
•Modeling nonlinear change
Day 4
•Predicting change over time with time-invariant (time-stable) covariates
•Intercepts and slopes as outcomes; Probing cross-level interactions
•When predictors also change over time
•Incorporating time-varying covariates into a growth model
•Time-specific “shocks”; Within- and between-person effects
•Multivariate growth models
•Studying how variables track together through time
Day 5
•Models for intensive short-term longitudinal data
•Incorporating more complex error structures
•Modeling transitions or turning points (piecewise models)
•Generalized nonlinear models for binary outcomes
•Random intercept and slope models for binary outcomes
•Longitudinal growth models for binary outcomes
•Self-Study: Overview of advanced topics
•Testing mediation in multilevel models: direct and indirect effects
•Three level data
•Cross-classified data