Multilevel Modeling

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

Read reviews of this seminar


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 7 and Saturday, April 8 at Temple University Center City, 1515 Market Street, Philadelphia, PA 19102. 

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. The early registration fee of $895 is available until March 7.

Refund Policy
If you cancel your registration at least two weeks before the course is scheduled to begin, you are entitled to a full refund (minus a processing fee of $50). 

Lodging Reservation Instructions

A block of guest rooms has been reserved at the Club Quarters Hotel, 1628 Chestnut Street, Philadelphia, PA at a special rate of $154 per night. This location is about a 5 minute walk to the seminar location. In order to make reservations, call 203-905-2100 during business hours and identify yourself as part of the Statistical Horizons room block. For guaranteed rate and availability, you must reserve your room no later than Monday, March 06, 2017.

If you make reservations after the cut-off date ask for the Statistical Horizons room rate (do not use the code) and they will try to accommodate your request.


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 was intimidated to take this course, thinking it would be over my head. However, Dr. Raykov is an excellent instructor and I have been able to learn a lot more than expected. His ability to explain concepts and build equations from simple to complex makes it easier to understand various models and their outputs. Highly recommend to others.”
  Sarah Klieger, Temple University

“This is an excellent intermediate course on Multileveling Modeling, especially valuable for students/practitioners who are familiar with MLM but not how to do it in Stata. Many good examples of MLM application on differing datasets, programming already performed.”
  David Armor, George Mason University

“Dr. Raykov took a highly complex topic i.e. Multilevel Modeling, and tore it down into the fundamental building blocks of regression we are all used to. Masterful. Highly recommend this course.”
  Anonymous 

“Linking the concepts with STATA code and results from example datasets was very helpful.”
  Michael Monuteaux, Boston Children’s Hospital

“Tenko is a fantastic instructor, whose materials are clear and concise. It was easy to digest a broad range of topics and problems in a very short period of time. For anyone who is trying to learn these topics most efficiently, I would highly recommend this course.”
  Ashley Martin, Columbia Business School