Categorical Data Analysis

taught by Brian Marx

This course will cover the analysis of contingency table data (tabular data in which the cell entries represent counts of subjects or items falling into certain categories). 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.

Anyone who needs to analyze data in which the response is in categorical form. Market researchers, medical researchers, surveyors, those who study education assessment data, quality control specialists, life scientists, environmental scientists, ecologists.

Categorical Data Analysis

taught by Brian Marx

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Courses may fill up at any time and registrations are processed in the order in which they are received. Your registration will be confirmed for the first available course date, unless you specify otherwise. Multiple course registrations may be entitled to tuition discounts; read more.

Categorical Data Analysis

taught by Brian Marx

This course will cover the analysis of contingency table data (tabular data in which the cell entries represent counts of subjects or items falling into certain categories). 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. A modeling approach to categorical data analysis will also be presented, which is motivated through special cases of the generalized linear model, specifically Poisson regression for count responses and logistic/ probit regression for binomial responses. The focus will be on interpretation of models rather than the theory behind them.

See also: Categorical Data Analysis - Applied Modeling.

If you are unclear as to whether you have mastered the requirements, try these placement tests here.

SESSION 1: Overview of Categorical Responses

• Binomial and multinomial distributions
• Maximum Likelihood
• Test of proportions

SESSION 2: Structure of Contingency Tables

• Joint, marginal and conditional probabilities
• Odds ratio and relative risk
• Test of independence
• Ordinal data
• Exact testing
• Three-way tables
• Conditional independence and homogenous association

SESSION 3: Generalized Linear Models

• Binary data: logistic and probit models
• Poisson regression for count data
• Model checking

SESSION 4: Applications and Interpretations for Logistic Regression

• Inference
• Categorical predictors
• Multiple logistic regression

HOMEWORK:

Homework in this course consists of short answer questions to test concepts and guided numerical problems using software.

This course takes place over the internet at the Institute for 4 weeks. During each course week, you participate at times of your own choosing - there are no set times when you must be online. Course participants will be given access to a private discussion board. In class discussions led by the instructor, you can post questions, seek clarification, and interact with your fellow students and the instructor.

The course typically requires 15 hours per week. At the beginning of each week, you receive the relevant material, in addition to answers to exercises from the previous session. During the week, you are expected to go over the course materials, work through exercises, and submit answers. Discussion among participants is encouraged. The instructor will provide answers and comments, and at the end of the week, you will receive individual feedback on your homework answers.

1. You may be interested only in learning the material presented, and not be concerned with grades or a record of completion.
2. You may be enrolled in PASS (Programs in Analytics and Statistical Studies) that requires demonstration of proficiency in the subject, in which case your work will be assessed for a grade.
3. You may require a "Record of Course Completion," along with professional development credit in the form of Continuing Education Units (CEU's).  For those successfully completing the course, 5.0 CEU's and a record of course completion will be issued by The Institute, upon request.

The required text for this course is An Introduction to Categorical Data Analysis, Second Edition by Alan Agresti, and it can be ordered from Wiley by clicking here. Wiley typically offers statistics.com customers up to 15% discount on this book (and all other statistics titles): enter the code aff15 in the Promotion Code field when prompted during checkout and click the Apply Discount button. (If you are located in Asia, the web procedure for your location may not accept this discount – try calling your regional Wiley representative.).

PLEASE ORDER YOUR COPY IN TIME FOR THE COURSE STARTING DATE.

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; click here for information on obtaining a free (or nominal cost) copy of various software packages for use during the course.

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