Categorical Data Analysis

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.

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.

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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.

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.

Instructors

dr-brian-marx

Dr. Brian Marx

Dr. Brian Marx is Professor of Statistics at Louisiana State University, and has taught Categorical Data Analysis for over ten years. He is currently serving as Chair of the Statistical Modelling Society and is the Coordinating Editor of Statistical Modelling: An International Journal. Dr. Marx has numerous publications in peer reviewed journals.
See Instructor Bio

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

2019

Oct 4, 2019 to Nov 1, 2019

2020

Apr 3, 2020 to May 1, 2020

Oct 2, 2020 to Oct 30, 2020

2021

No classes scheduled at this time.

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Prerequisites

The courses listed below are prerequisites for enrollment in this course:

Regression Analysis

This course will teach you how multiple linear regression models are derived, assumptions in the models, how to test whether data meets assumptions, and develop strategies for building and understanding useful models.
Topic: Statistics, Statistical Modeling | Skill: Intermediate | Credit Options: ACE, CAP, CEU
Class Start Dates: Jan 17, 2020, May 8, 2020, Oct 2, 2020

Statistics 1 – Probability and Study Design

This course, the first of a three-course sequence, provides an introduction to statistics for those with little or no prior exposure to basic probability and statistics.
Topic: Statistics, Introductory Statistics | Skill: Introductory | Credit Options: ACE, CAP, CEU

Statistics 2 – Inference and Association

This course, the second of a three-course sequence, will teach you the use of inference and association through a series of practical applications, based on the resampling/simulation approach, and how to test hypotheses, compute confidence intervals regarding proportions or means, computer correlations, and use of simple linear regressions.
Topic: Statistics, Introductory Statistics | Skill: Introductory | Credit Options: ACE, CEU
Class Start Dates: Dec 6, 2019

What Our Students Say​

Brian Marx is the best teacher I have had. The homework was straight forward and Brian responded to questions on the discussion board in a timely fashion.

Suzette Blanchard
Senior Biostatistician/Assistant Professor, Division of Biostatistics, City of Hope

This is my fourth course that I have taken at Statistics.com and I am very satisfied with the level and quality of instruction. I will continue to look for: courses that I can take whenever possible.

Herman Chavarria
Kinetic Concepts Inc.

Frequently Asked Questions

Can I transfer or withdraw from a course?

We have a flexible transfer and withdrawal policy that recognizes circumstances may arise to prevent you from taking a course as planned. You may transfer or withdraw from a course under certain conditions.

  • Students are entitled to a full refund if a course they are registered for is canceled.
  • You can transfer your tuition to another course at any time prior to the course start date or the drop date, however a transfer is not permitted after the drop date.
  • Withdrawals on or after the first day of class are entitled to a percentage refund of tuition.

Please see this page for more information.

Who are the instructors at the Institute?

The Institute has more than 60 instructors who are recruited based on their expertise in various areas in statistics. Our faculty members are:

  • Authors of well-regarded texts in their area;
  • Advisory board members;
  • Senior faculty; and
  • Educators who have made important contributions to the field of statistics or online education in statistics.

The majority of our instructors have more than five years of teaching experience online at the Institute.

Please visit our faculty page for more information on each instructor at The Institute for Statistics Education.

Please see our knowledge center for more information.

What type of courses does the Institute offer?

The Institute offers approximately 80 courses each year. Topics include basic survey courses for novices, a full sequence of introductory statistics courses, bridge courses to more advanced topics. Our courses cover a range of topics including biostatistics, research statistics, data mining, business analytics, survey statistics, and environmental statistics.

Please see our course search or knowledge center for more information.

Do your courses have for-credit options?

Our courses have several for-credit options:

  • Continuing education units (CEU)
  • College credit through The American Council on Education (ACE CREDIT)
  • Course credits that are transferable to the INFORMS Certified Analytics Professional (CAP®)

Please see our knowledge center for more information.

Is the Institute for Statistics Education certified?

The Institute for Statistics Education is certified to operate by the State Council of Higher Education for Virginia (SCHEV). For more information visit: http://www.schev.edu

Please see our knowledge center for more information.

Visit our knowledge base and learn more.

FAQs + Knowledge Base

Related Courses

Logistic Regression

This course will teach you logistic regression ordinary least squares (OLS) methods to model data with binary outcomes rather than directly estimating the value of the outcome, logistic regression allows you to estimate the probability of a success or failure.
Topic: Statistics, Statistical Modeling | Skill: Intermediate | Credit Options: CAP, CEU
Class Start Dates: Jan 10, 2020, Jul 17, 2020

Modeling Count Data

This course will teach you regression models for count data, models with a response or dependent variable data in the form of a count or rate, Poisson regression, the foundation for modeling counts, and extensions and modifications to the basic model.
Topic: Statistics, Statistical Modeling | Skill: Intermediate, Advanced | Credit Options: CAP, CEU
Class Start Dates: Oct 23, 2020

Additional Course Information

Organization of Course

This course takes place online 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.

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.

Time Requirements

This is a 4-week course requiring 10-15 hours per week of review and study, at times of your choosing.

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.

Please order a copy of your course textbook prior to course start date.

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. 

Software Uses and Descriptions | Available Free Versions
To learn more about the software used in this course, or how to obtain free versions of software used in our courses, please read our knowledge base article “What software is used in courses?” 

Course Fee & Information

Enrollment
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.

Transfers and Withdrawals
We have flexible policies to transfer to another course or withdraw if necessary.

Group Rates
Contact us to get information on group rates.

Discounts
Academic affiliation?  In most courses you are eligible for a discount at checkout.

New to Statistics.com?  Click here for a special introductory discount code.  

Invoice or Purchase Order
Add $50 service fee if you require a prior invoice, or if you need to submit a purchase order or voucher, pay by wire transfer or EFT, or refund and reprocess a prior payment.

Options for Credit and Recognition

This course is eligible for the following credit and recognition options:

No Credit
You may take this course without pursuing credit or a record of completion.

Mastery or Certificate Program Credit
If you are enrolled in mastery or certificate program that requires demonstration of proficiency in this subject, your course work may be assessed for a grade.

CEUs and Proof of Completion
If you require a “Record of Course Completion” along with professional development credit in the form of Continuing Education Units (CEU’s), upon successfully completing the course, CEU’s and a record of course completion will be issued by The Institute upon your request.

ACE CREDIT | College Credit
This course has been evaluated by the American Council on Education (ACE) and is recommended for the the upper division baccalaureate degree, 3 semester hours in data mining, statistics, or computer science. Please note that the decision to accept specific credit recommendations is up to the academic institution accepting the credit.

ACE Digital Badge
Courses evaluated by the American Council on Education (ACE) have a digital badge available for successful completion of the course.

Supplemental Information

There is no supplemental content for this course.

Miscellaneous

There is no additional information for this course.

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Categorical Data Analysis
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