Multilevel Modeling of Categorical Outcomes

A 2-Day Seminar Taught by Donald Hedeker, Ph.D.

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To see a sample of the course materials, click here.


Multilevel models are increasingly used for analysis of clustered and longitudinal data, and methods for continuous outcomes are commonly used and applied. However, many research studies have non-normal outcomes, for example, outcomes that are dichotomous, ordinal, or nominal. Although methods for such non-normal outcomes have been available for quite some time, they are perhaps not as routinely applied as models for continuous outcomes.

This workshop will focus on analysis of dichotomous, ordinal and nominal multilevel outcomes. Both clustered and longitudinal data will be considered, and the following models will be described: multilevel logistic regression for dichotomous outcomes, multilevel logistic regression for nominal outcomes, and multilevel proportional odds and non-proportional odds models for ordinal outcomes. The latter models are useful because the proportional odds assumption of equal covariate effects across the cumulative logits of the model is often inconsistent with the data.


Computing

In all cases, methods will be illustrated using software, with SAS, Stata, and SuperMix examples and syntax. Some familiarity with reading in data and performing basic statistical analyses in either SAS or Stata is recommended.

This is a hands-on course with at least one hour each day devoted to carefully structured and supervised assignments. To do the exercises, you will need to bring your own laptop computer with a recent version of SAS or Stata and the free student version of SuperMix installed. SuperMix can be downloaded here

NOTE: If you have Windows 7 or older, please run SuperMix as administrator. Windows 8 and more recent should run as a regular user.


Who should attend? 

If you plan on analyzing multilevel data (either clustered or longitudinal) and have categorical outcomes of interest, this course is for you. Analyzing multilevel categorical data is necessary in many research fields where normally distributed continuous outcomes are not obtained or relevant.    

Participants should be thoroughly familiar with multiple linear regression, and have some knowledge of logistic regression.


LOCAtion, Format, And Materials 

The class will meet from 9 am to 5 pm each day with a 1-hour lunch break at Temple University Center City, 1515 Market Street, Philadelphia, PA 19103. 

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 May 11.

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 $169 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 by using group code STH610 or click here. For guaranteed rate and availability, you must reserve your room no later than Monday, May 11, 2020.

If you need to make reservations after the cut-off date, you may call Club Quarters directly and ask for the “Statistical Horizons” rate (do not use the code or mention a room block) and they will try to accommodate your request.


SEMINAR OUTLINE

  1. Introduction to multilevel analysis
  2. Multilevel analysis of clustered dichotomous outcomes
  3. Multilevel analysis of clustered ordinal outcomes
  4. Testing the proportional odds assumption
  5. Multilevel non-proportional odds models for ordinal data
  6. Multilevel analysis of longitudinal dichotomous outcomes
  7. Subject-specific and population-averaged estimates
  8. Marginalization of the subject-specific estimates
  9. Multilevel analysis of longitudinal ordinal outcomes
  10. Multilevel analysis of longitudinal nominal outcomes

RECENT COMMENTS FROM PARTICIPANTS

“Professor Hedeker is excellent. I really liked his ability to cover a lot of material and do it clearly. This is a weak area of mine and he helped fill in many of the gaps in my own understanding.”
  Richard Williams, University of Notre Dame

“Dr. Hedeker put very rich contents into this class. Students are allowed to raise questions at any time through the class. This helps students to catch the important points in a timely manner and be able to get through the learning easily.”
  Qin Liu, The Wistar Institute

“This class offers an in-depth discussion of the theory, fitting, and interpretation of multilevel modeling for non-normal data that does not seem to exist elsewhere. Don puts together and delivers arguably the best short course I’ve ever taken. This course is well worth the time and money.”
  Amy Hughes, University of Texas

“I found Don’s course very helpful for advanced as well as intermediate analysis of categorical data in clustered structures. There were many tips, tricks, nuances, and insights communicated from Don’s many years of experience with categorical data problems. I am taking away many helpful strategies for approaching my ongoing projects.”
  Andrea Howard, Carleton University