CATEGORICAL DATA ANALYSIS A complete five-day course on the analysis of discrete data using SAS® Here are a few of the things you'll learn in this course What's wrong with ordinary linear regression when the dependent variable is a dichotomy. Why ordinary regression is sometimes OK. The easy and intuitive way to interpret logit coefficients. Why logit coefficients are inherently standardized. How to analyze contingency tables with a logistic regression program. How to compare logit coefficients across groups, while adjusting for unobserved heterogeneity. Why chi-square statistics should sometimes be ignored. Why logistic regression is usually preferred to probit regression. When to use the complementary log-log model. Why logistic regression models sometimes fail to converge--and what you can do about it. How sampling on the dependent variable can give sometimes give you better estimates. How to estimate and interpret a multinomial logit model. How you can fit a multinomial model with a binary logit program. How to choose among three different approaches to ordinal dependent variables. How to control for all constant characteristics of individuals using panel data. Why marginal tables are so important in log-linear analysis. How to convert a log-linear model to a logit model and vice versa. Who should attend? If you need to analyze categorical data and have a basic statistical background, this course is for you. You should have a good working knowledge of the principles and practice of multiple regression, as well as elementary statistical inference. But you do not need to know matrix algebra, calculus, or likelihood theory. The course will stress generalized regression models with categorical dependent variables. Nevertheless, there will also be some treatment of two-way contingency tables in which neither variable has causal or predictive priority. The course does emphasize SAS® rather heavily.  No previous knowledge of SAS is assumed, however. Furthermore, nearly all the techniques taught in the course can be translated fairly easily to other packages. Course Outline 1. Traditional methods for categorical data analysis 2. Review of linear model 3. OLS regression with categorical covariates 4. Dichotomous dependent variables in OLS regression 5. Heteroscedasticity and nonnormality 6. Weighted least squares estimates of linear probability model 7. Nonlinearity 8. The logit model 9. Estimating the logit model with grouped data 10. Estimating the logit model with ungrouped data 11. Maximum likelihood estimation 12. Interpreting logit coefficients 13. Similarities with multiple regression 14. Nonconvergence of ML estimates 15. Probit model and other link functions 16. Latent variable interpretation of the models 17. Unobserved heterogeneity 18. Logit and probit analysis for contingency tables 19. Goodness-of-fit tests for grouped data 20. Multinomial response models : unordered case 21. Logit models for ordered polytomies 22. Uniform association model 23. Cumulative logit and probit 24. Continuation ratio models 25. Latent variable interpreation 26. Response-based sampling 27. Poisson regression 28. Panel data 29. Random effects models 30. GEE estimation 31. Fixed effects logit model 32. Event history models 33. Discrete choice models 34. Loglinear models for contingency tables 35. Importance of marginal tables in loglinear models 36. Special loglinear models for mobility (and other 2-way) tables Computing The course will focus on five SAS® procedures for categorical data analysis: LOGISTIC,  SURVEYLOGISTIC, GENMOD, GLIMMIX and NLMIXED. At least one hour each day is devoted to carefully structured and supervised assignments on personal computers.  Additional time is available for exploring other sample data sets. Or you can bring your own data and try out new techniques as you learn them. Location, format, materials The course meets Monday through Friday at Temple University Center City, located in the heart of Philadelphia. Here is a typical day's schedule: 9-12 Lecture 12-1 Lunch break 1-3 Lecture 3-5 Computing and consulting The textbook for the course is Professor Allison's book, Logistic Regression Using the SAS® System. In addition to the textbook, participants will receive a 100-page manual containing detailed lecture notes (with equations and graphics), examples of computer printout, and many other useful features. This book frees participants from the distracting task of notetaking.   Registration and Lodging The fee of $1400 includes all course materials and computing costs. To ensure your participation, click here to register . Cancellations received two weeks before the course begins are fully refundable (minus a $100 processing fee if you paid by credit card).  Participants must make their own arrangements for lodging and meals. A special rate has been arranged at a nearby hotel. Evaluations from Previous Participants Participants in the July 2008 seminar were asked to rate the course on a scale of 1 (worst) to 10 (best).  The average score for 21 respondents was 9.0.  The following comments were also received: “Excellent course.  Material is presented by Paul in a manner that complicated concepts can be understood by most people.  Information presented in the course can immediately be applied to data analysis by everyone in different subject areas. Worth every $.”  Jose Santos, University of Florida   “This is the best training I have every gotten.  I would rate this with the highest rank.  It helps me with both methodology and the programming aspects of the class.” Li Zhou, Verispan   “This course was very informative and covered a wide range of topics.  I would definitely recommend it to any researcher who uses categorical data analysis.” Nehama Lewis, University of Pennsylvania     “Dr. Paul gave us clear ideas on how to use the logistic regression model in categorical data analysis.  It’s very helpful for my market research study.” Jianqing Gao, Verispan   “Excellent course for anyone out there who wants to take a deeper dive into logistic regression.  The course covers general examples making it applicable to almost any industry.  I would definitely recommend it.” Willys Otieno, Addison, Texas    “Time well spent for participants with functional tools in statistics.  Provides a knowledge base to students that allows them to be both independent and to communicate with resident statisticians to properly analyze experimental data in various disciplines.” William Thatcher, University of Florida   “It’s very helpful in applications.  This course tells you what procedures/statements/methods are appropriate for your data and which are not.  Good examples.”  Wei Wang, Nationwide Children’s Hospital   “The instructor is very knowledgeable about the topic.  The course covers a wide range of models.  The textbook will serve as an excellent reference for the students.” Yihua Gu, Abbott