Multilevel Modeling of Categorical Outcomes - Online Course
A 4-Day Livestream Seminar Taught by
Donald Hedeker10:30am-12:30pm (convert to your local time)
1:30pm-3:00pm
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, counts, 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, nominal, and count 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, multilevel Poisson regression for counts, 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.
Starting June 26, we are offering this seminar as a 4-day synchronous*, livestream workshop held via the free video-conferencing software Zoom. Each day will consist of two lecture sessions which include hands-on exercises, separated by a 1-hour break. You are encouraged to join the lecture live, but will have the opportunity to view the recorded session later in the day if you are unable to attend at the scheduled time.
*We understand that finding time to participate in livestream courses can be difficult. If you prefer, you may take all or part of the course asynchronously. The video recordings will be made available within 24 hours of each session and will be accessible for four weeks after the seminar, meaning that you will get all of the class content and discussions even if you cannot participate synchronously.
Closed captioning is available for all live and recorded sessions. Live captions can be translated to a variety of languages including Spanish, Korean, and Italian. For more information, click here.
Computing
In all cases, methods will be illustrated using software, with SAS and Stata 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 class involving several structured and supervised assignments. To do the exercises, you will need a computer with a recent version of SAS or Stata.
There is now a free version of SAS, called SAS OnDemand for Academics, that is available to anyone.
If you’d like to familiarize yourself with Stata basics before the seminar begins, we recommend following along with a “getting started” video like the one here.
Seminar participants who are not yet ready to purchase Stata could take advantage of StataCorp’s free 30-day evaluation offer or their 30-day software return policy.
In all cases, methods will be illustrated using software, with SAS and Stata 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 class involving several structured and supervised assignments. To do the exercises, you will need a computer with a recent version of SAS or Stata.
There is now a free version of SAS, called SAS OnDemand for Academics, that is available to anyone.
If you’d like to familiarize yourself with Stata basics before the seminar begins, we recommend following along with a “getting started” video like the one here.
Seminar participants who are not yet ready to purchase Stata could take advantage of StataCorp’s free 30-day evaluation offer or their 30-day software return policy.
Who should register?
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.
You should be thoroughly familiar with multiple linear regression, and have some knowledge of logistic regression.
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.
You should be thoroughly familiar with multiple linear regression, and have some knowledge of logistic regression.
Seminar outline
Day 1
1. Introduction to multilevel data
-
- What are multilevel data?
- Why do multilevel data analysis?
- Overview of multilevel models for categorical data.
2. Multilevel analysis for clustered binary outcomes
-
- Multilevel logistic regression model.
- Scaling of regression coefficients.
- Students in classrooms and schools example.
- Using Stata, SAS, and Supermix for multilevel analysis.
- Interpreting output, testing for cluster effects, creating plots of cluster effects.
- Fit of models to observed proportions.
Day 2
3. Multilevel models for clustered ordinal outcomes
-
- Multilevel cumulative logistic regression model.
- Proportional odds assumption.
- Students in classrooms and schools example.
- Using Stata, SAS, and Supermix for multilevel ordinal analysis.
- Interpreting output, and testing for cluster effects.
- Fit of models to observed proportions.
- Non-proportional odds multilevel models.
4. Introduction to multilevel models for longitudinal binary outcomes
-
- Multilevel logistic regression model.
- Random intercept and trend models.
- Subject-specific and population-averaged estimates.
- Longitudinal psychiatric clinical trials example – descriptive statistics and plots.
Day 3
5. Multilevel for longitudinal binary outcomes (continued)
-
- Longitudinal psychiatric clinical trials example – random intercept and trend models.
- Using Stata, SAS, and Supermix for multilevel analysis.
- Conditional and marginalized estimates.
- Interpreting output, testing for random subject effects, creating plots.
- Fit of models to observed proportions.
6. Multilevel models for longitudinal ordinal outcomes
-
- Multilevel cumulative logistic regression model.
- Proportional odds assumption
- Random intercept and trend models.
- Longitudinal psychiatric clinical trials example – descriptive statistics and plots.
- Using Stata, SAS, and Supermix for multilevel analysis.
- Conditional and marginalized estimates.
- Interpreting output, testing for random subject effects, creating plots.
- Fit of models to observed proportions.
Day 4
7. Multilevel models for longitudinal ordinal and nominal outcomes
-
- Longitudinal homelessness study – descriptive statistics and plots.
- Using Stata, SAS, and Supermix for multilevel analysis.
- Non proportional odds models; testing proportional odds assumption.
- Treating outcome as nominal; reference cell comparisons.
- Interpreting output, testing for random subject effects, creating plots.
- Fit of models to observed proportions.
8. Multilevel analysis of longitudinal count outcomes
-
- Multilevel Poisson regression model.
- Number of headaches and Aspartame crossover study.
- Using Stata, SAS, and Supermix for multilevel Poisson analysis.
- Interpreting output, and testing for random subject effects.
- Fit of models.
- Overdispersion.
Day 1
1. Introduction to multilevel data
-
- What are multilevel data?
- Why do multilevel data analysis?
- Overview of multilevel models for categorical data.
2. Multilevel analysis for clustered binary outcomes
-
- Multilevel logistic regression model.
- Scaling of regression coefficients.
- Students in classrooms and schools example.
- Using Stata, SAS, and Supermix for multilevel analysis.
- Interpreting output, testing for cluster effects, creating plots of cluster effects.
- Fit of models to observed proportions.
Day 2
3. Multilevel models for clustered ordinal outcomes
-
- Multilevel cumulative logistic regression model.
- Proportional odds assumption.
- Students in classrooms and schools example.
- Using Stata, SAS, and Supermix for multilevel ordinal analysis.
- Interpreting output, and testing for cluster effects.
- Fit of models to observed proportions.
- Non-proportional odds multilevel models.
4. Introduction to multilevel models for longitudinal binary outcomes
-
- Multilevel logistic regression model.
- Random intercept and trend models.
- Subject-specific and population-averaged estimates.
- Longitudinal psychiatric clinical trials example – descriptive statistics and plots.
Day 3
5. Multilevel for longitudinal binary outcomes (continued)
-
- Longitudinal psychiatric clinical trials example – random intercept and trend models.
- Using Stata, SAS, and Supermix for multilevel analysis.
- Conditional and marginalized estimates.
- Interpreting output, testing for random subject effects, creating plots.
- Fit of models to observed proportions.
6. Multilevel models for longitudinal ordinal outcomes
-
- Multilevel cumulative logistic regression model.
- Proportional odds assumption
- Random intercept and trend models.
- Longitudinal psychiatric clinical trials example – descriptive statistics and plots.
- Using Stata, SAS, and Supermix for multilevel analysis.
- Conditional and marginalized estimates.
- Interpreting output, testing for random subject effects, creating plots.
- Fit of models to observed proportions.
Day 4
7. Multilevel models for longitudinal ordinal and nominal outcomes
-
- Longitudinal homelessness study – descriptive statistics and plots.
- Using Stata, SAS, and Supermix for multilevel analysis.
- Non proportional odds models; testing proportional odds assumption.
- Treating outcome as nominal; reference cell comparisons.
- Interpreting output, testing for random subject effects, creating plots.
- Fit of models to observed proportions.
8. Multilevel analysis of longitudinal count outcomes
-
- Multilevel Poisson regression model.
- Number of headaches and Aspartame crossover study.
- Using Stata, SAS, and Supermix for multilevel Poisson analysis.
- Interpreting output, and testing for random subject effects.
- Fit of models.
- Overdispersion.
Payment information
The fee of $995 includes all course materials.
PayPal and all major credit cards are accepted.
Our Tax ID number is 26-4576270.
The fee of $995 includes all course materials.
PayPal and all major credit cards are accepted.
Our Tax ID number is 26-4576270.