GENERALIZED LINEAR MODELS & GENERALIZED ESTIMATING EQUATIONS 2013An introductory, graduate-level illustrated tutorial on generalized linear models and generalized estimating equations usuing SPSS. SAS, and Stata. Covers linear regression, gamma regression, binary logistic regression, binary probit regression, Poisson regression, log-linear analysis, negative binomial regression, ordinal logistic regression, ordinal probit regression, complementary log-log regression, and other GZLM models. Also covers repeated measures linear regression, repeated measures binary logistic regression, and other GEE models.Partial Table of ContentsKey Concepts and Terms12Types of data distributions13Types of link functions19Types of estimation methods26Statistical measures26Goodness of fit statistics27Likelihood ratio tests32Deviance ratios (scaled deviance)33Tests of model effects33Parameter estimates34Odds ratios36Pseudo R-square and other effect size measures38Contrast coefficients39User interfaces for GZLM42GZLM Models61Linear regression62Binary logistic regression91Binary probit regression109Complementary log-log (cloglog) models118Ordinal logistic regression130Ordinal probit regression142Gamma regression149Poisson regression170Poisson count models, rate models, and loglinear models170A negative binomial model as an alternative172Negative binomial regression193Mixture (Tweedie) models200GENERALIZED ESTIMATING EQUATIONS (GEE)201What is GEE?201Assumptions of GEE203Statistical packages and GEE205Types of GEE model205Subject and within-subject variables206Unbalanced designs207The assumed (working) correlation matrix207Goodness of fit measures in GEE211Data structure for GEE211Data Examples212Repeated measures linear regression using GEE212Repeated measures binary logistic regression214Residual analysis263Variables available in GEE263Variables available in GZLM but not GEE264Assumptions265Frequently Asked 292