Categorical Data - Applied Modeling

taught by Brian Marx

Brief Description:

This course continues the analysis of categorical data, contains a review of logistic regression, and introduces multinomial responses for logistic regression, probit, logit and loglinear analysis.

Instructor(s):
Level: intermediate/advanced

Who Should Take This Course:

Any researcher or analyst who encounters categorical data and needs to analyze or model it.

Dates:
November 09, 2012 to December 07, 2012November 08, 2013 to December 06, 2013
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Registration:
Please read the syllabus tab, noting the prerequisites, text and software requirements.

Register Online -$499
Register Online -$399 (you must be affiliated with a college, university or high school)

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Categorical Data - Applied Modeling

taught by Brian Marx

Aim of Course:

This course continues the survey of categorical data analysis begun in Categorical Data Analysis. After taking this course, students will know how to perform logistic regression (with both binomial and multinomial response), probit, logit and loglinear analysis using statistical software. Model diagnostics and interpretation of results are also covered, and longitudinal analysis is introduced.

Note: Some of the topics in this course, e.g. logistic regression, GLM, have their own courses at statistics.com that treat the subject in greater depth. "Categorical Data Analysis" and and "Categorical Data Analysis - Applied Modeling" emphasize the use of statistical software to construct and assess models.

Prerequisite(s):

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

Participants should also be familiar with the material in Categorical Data Analysis.

Course Program:

SESSION 1: Logistic Regression Review

• Interpretation of parameters and odds ratio
• Standard errors
• Probit analysis
• Variable selection
• Multiple logistic regression with categorical predictors
• Building and applying logit models
• Model selection (backward elimination)
• Model checking
• Sparse data

SESSION 2: Multicategory Logit Models

• Logistic regression with multinomial response
• proportional odds models

SESSION 3: Loglinear Models for Contingency Tables

• Loglinear models for independence
• Association
• 3-way and 4-way tables
• Connections
• Logit models
• Analysis of deviance
• Graphical modeling
• Linear by linear association
• Models for ordinal responses

SESSION 4: Model for Matched Pairs

• McNemar test
• Symmetry models
• Quasi-symmetry
• Ordinal quasi-symmetry
• Quasi-independence models
• Rater agreement
• Bradley-Terry model

HOMEWORK:

Homework in this course consists of short answer questions to test concepts. guided data analysis problems using software, and guided data modeling problems using software.

Organization of the Course:

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.

Credit:
Students come to the Institute for a variety of reasons. As you begin the course, you will be asked to specify your category:

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.

Course Text:

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.

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

Register Now

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Categorical Data - Applied Modeling

taught by Brian Marx

Instructor(s):
Dates:
November 09, 2012 to December 07, 2012November 08, 2013 to December 06, 2013
Course Fee: $499
Academic Rate: $399

Before registering, please read the syllabus tab, noting the prerequisites, text and software requirements. When you click the register button, you will be taken to our secure transaction page.

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