Ed231C: Applied Categorical
& Nonnormal Data Analysis
Instructor: Phil Ender
TBA -- Moore Hall TBA
The course will provide an introduction to the fundamental concepts
of categorical and other nonnormal data analysis. The course will
emphasize the application and interpretation of analyses of logistic,
ordered logistic, multinomial logistic, poisson, negative binomial
and other models. The course is designed for graduate students from
all areas of education and the social sciences. The presentation will be
non-mathematical emphasizing concepts and understanding. The
course is aimed at practitioners and researchers. The course
prerequisite is Education 230B (multiple regression) or its
equivalent.
Topics Include: |
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Logistic regression
Complementary log-log models
Ordered logit models
Poisson regression
Zero inflated poisson
Bivariate probit
Instrumental variable models
Generalized linear models
Regression with censored data
Discrete time survival analysis |
Probit analysis
Conditional logit models
Multinomial logit models
Negative binomial regression
Zero inflated negative binomial
Multivariate probit
Regression with selection
Generalized estimating equations
Regression with truncated data
Survival analysis |
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