Categorical Data Analysis
This course will teach you the analysis of contingency table data. Topics include tests for independence, comparing proportions as well as chi-square, exact methods, and treatment of ordered data. Both 2-way and 3-way tables are covered.
Overview
This course focuses on a logistic regression approach for analyzing contingency table data, where the cell entries represent counts that are cross-tabulated using categorical variables. It lays the groundwork for logistic regression models for binomial responses and goes on to introduce more complex data structures, e.g. those with more categorical variables or continuous covariates. Students get a broad view of the generalized linear model framework, and are also exposed to several model variations. This course is laser-focused on logistic regression modeling and how to interpret these models, rather than the theory behind them.
- Intermediate
- 4 Weeks
- Expert Instructor
- Tuiton-Back Guarantee
- 100% Online
- TA Support
Learning Outcomes
Students who complete this course will be able to:
- Construct R by C tables when given counts
- Calculate joint, marginal and conditional probabilities
- Test for independence, and equality of proportions
- Fit logistic models for binary data
- Fit Poisson models for count data
- Check model assumptions and analyze residuals and goodness-of-fit
- Conduct inference for model parameters
- Interpret the output of a logistic model
- Handle both grouped and ungrouped data
- Use variable selection algorithms (stepwise, etc.) to reduce the number of predictors
- Deal with the effects of sparse data
Who Should Take This Course
Anyone who needs to analyze data in which the response is in yes/no or categorical form. Market researchers, medical researchers, surveyors, those who study education assessment data, quality control specialists, life scientists, environmental scientists, ecologists.
Our Instructors
Dr. Nand Kishore Rawat
Dr. Nand Kishore Rawat is a Portfolio Director of Clinical Research Services at Cytel Statistical Software & Services. With his expertise in planning, designing and analysing clinical trial projects, and his experience in clinical trial development at Bristol-Myers Squibb, Novartis, and also noted contract research organizations (CRO’s), he brings a valuable practical perspective to the classroom and gives his students an insightful window into the pharmaceutical clinical trial world. Dr. Nand Kishore Rawat has expertise in planning, designing and analysing clinical trial projects.
Course Syllabus
Week 1
Categorical Responses and Contingency Tables
- Binomial and multinomial distributions
- Maximum Likelihood
- Test of proportions
- Joint, marginal and conditional probabilities
- Odds ratio and relative risk
- Test of independence
- Three-way tables
- Conditional independence and homogenous association
Week 2
Generalized Linear Models
- Components of a generalized linear model
- Binary data: logistic and probit models
- Poisson regression for count data
- Model checking and residual analysis
- Inference about model parameters
- Goodness-of-fit and deviance
Week 3
Applications and Interpretations for Logistic Regression
- Interpretation in logistic regression
- Odds-ratio, EL50, probability rate of change
- Inference and confidence intervals for logistic regression
- Grouped and ungrouped data
- Categorical predictors/ indicator variables/ coding
- Multiple logistic regression
Week 4
Building and Applying Logistic Regression Models
- Strategies in model selection
- Model checking and AIC
- Forward, stepwise, backward algorithms
- Likelihood ratio testing for models
- Deviance and residuals assessment
- Effects of sparse data
Class Dates
2025
Instructors: Dr. Nand Kishore Rawat
Instructors: Dr. Nand Kishore Rawat
Prerequisites
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Additional Information
Homework
Homework in this course consists of short answer questions to test concepts and guided numerical problems using software.
In addition to assigned readings, this course also has supplemental readings available online, example software files, and an end of course data modeling project.
Course Text
The required text for this course is An Introduction to Categorical Data Analysis, Third Edition by Alan Agresti.
Software
Most standard software packages can do various forms of categorical data analysis. No one particular software program is required or used predominantly for course illustrations, but this course does require software that can do tests and confidence intervals for proportions, chi-square tests, and logistic regression. Standard packages such as SAS, Stata, R, SPSS, and Minitab can do this.
If you are planning to use R in this course and are not already familiar with it, please consider taking one of our courses where R is introduced from the ground up. R has a learning curve that is steeper than that of most commercial statistical software.
Options for Credit and Recognition
ACE CREDIT | College Credit
This course has been evaluated by the American Council on Education (ACE) and is recommended for the upper-division baccalaureate degree, 3 semester hours in statistics. Please note that the decision to accept specific credit recommendations is up to the academic institution accepting the credit.
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