Multilevel Modeling

A 2-Day Seminar Taught by Tenko Raykov, Ph.D

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Researchers in the behavioral, social, biomedical, business and economic disciplines often collect data that have a hierarchical structure. Patients are nested (clustered) within treatment centers, employees are nested within firms, respondents are nested within cities, students are nested within schools, and so on. As a consequence of this nesting, the observations in the data set are not statistically independent, thus violating a basic assumption of standard methods of analysis. Ignoring the nesting effect and proceeding with conventional, single-level methods of analysis (like linear regression) can yield highly misleading results. That’s because traditional methods produce standard errors that are typically too small, leading to confidence intervals that are too short and p-values that are too low.

This two-day seminar provides a thorough introduction to multilevel modeling, a statistical framework that accounts for the nesting effect and avoids these problems, as well as those associated with earlier methods of aggregation and disaggregation. Throughout the seminar, many empirical examples are drawn from the behavioral, clinical, educational and economic disciplines. The popular software package Stata is used for all the examples, along with a detailed discussion of the command syntax and interpretation of the output.

Participants in this seminar can expect to come away with:

1.  A nuanced understanding of the conceptual foundations and basic mathematical formulation of the multilevel model.
2.  The ability to understand, interpret and explain the output from multilevel modeling software.
3.  An appreciation of the advantages and disadvantages of multilevel modeling as compared with other approaches to nested data.
4.  Practical tools and strategies for developing and testing multilevel models.
5.  The ability to extend the multilevel model to dichotomous outcomes.
6.  A clear understanding of the differences between fixed and random effects.


Who should attend?

To benefit from this seminar, you should have the equivalent of two semesters of statistics: a good introductory course with some treatment of probability and random variables, and a course on linear models. Some knowledge of logistic regression will also be helpful but not essential.


COMPUTING

This seminar will use Stata for all examples, but prior knowledge of Stata is not essential. You are welcome to bring your own laptop computer, and outlets will be provided at each seat.  


Location, Format, and materials

The seminar meets Friday, April 1 and Saturday, April 2 at Temple University Center City, 1515 Market Street, Philadelphia, PA 19103. 

The class will meet from 9 to 4 each day with a 1-hour lunch break. 

Participants receive a bound manual containing detailed lecture notes (with equations and graphics), examples of computer printout, and many other useful features. This book frees participants from the distracting task of note taking. 


Registration and Lodging

The fee of $995 includes all course materials. 

Lodging Reservation Instructions

A block of rooms has been reserved at the Club Quarters Hotel, 1628 Chestnut St., Philadelphia, PA at a nightly rate of $152 for a Standard room. This hotel is about a 5-minute walk from the seminar location.  To make a reservation, you must call 203-905-2100 during business hours and use code STA331. For guaranteed rate and availability, you must make your reservation by March 1, 2016. 


Seminar outline

1. Resources for the seminar.
2. Why do we need multilevel modeling (MLM), and why are aggregation and disaggregation unsatisfactory?
3. The beginnings of MLM.
          – Why what we already know about regression analysis is so useful.
          – Centering of predictor variables.
4. The intra-class correlation coefficient – Do we really need a multilevel model?
5. How many levels? Proportions of third-level variance and at intermediate level, and how to evaluate them in three-level settings.
6. Random intercept models and model adequacy assessment.
7. Robust modeling of lower-level variable relationships in the presence of clustering effects.
8. What are mixed models, what are they made of, and why are they useful?
9. Random regression models – a general class of mixed/multilevel models of great utility great utility. 
          – Getting started and restricted maximum likelihood (REML) estimation.
          – Random regression models.
          – Multiple random slopes.
          – Fixed effects, random effects, and total effects.
          – Numerical issues and possible problems.
          – Nested levels (higher-order nesting).
10. Mixed models with discrete response variables – what to do when the outcome is not continuous? 
          – Why do we need another modeling approach?
          – Random intercept model with discrete outcome.
          – Random regression model with discrete response.
          – Model choice with discrete outcome.
11. Applications of multilevel modeling in complex design studies.
12. Conclusion and outlook.


Comments from recent participants  

“I have taken many statistics courses and this is one of the very best. Dr. Raykov’s understanding and communication of the material is outstanding. The setup and organization by Statistical Horizons was very professional. This is a great course!”
  Ryan Kettler, Rutgers, The State University of New Jersey 

“The instructor was friendly, energetic, and a wonderful teacher who really cared whether we were “getting it” as the course progressed. The course gave me a great foothold on the topic!”
  Rob Buschmann, University of Texas Medical Branch 

“The information in this course was presented in a very accessible way. Even though I am not a Stata user, I learned enough to try these models in SAS. Dr. Raykov was a great resource.
  Jerel Calzo, Boston Children’s Hospital/Harvard Medical School 

“The instructor is so helpful and great in terms of exploring complex models. This MLM course has been the best amongst many workshops I attended.”
  Giyeon Kim, The University of Alabama

“Very informative and extremely helpful. I learned cutting-edge knowledge on multi-level modeling.”
  Shaofeng Li, University of Auckland

“This course more than met my expectations. Beyond just MLM, I feel I learned some statistical theory I had wondered about. This material, I feel, is hard to learn from a book, so this kind of workshop is a godsend.”
  Bill Fisher, University of Massachusetts