This is the first book in longitudinal categorical data analysis with parametric correlation models developed based on dynamic relationships among repeated categorical responses. This book is a natural generalization of the longitudinal binary data analysis to the multinomial data setup with more than two categories. Thus, unlike the existing books on cross-sectional categorical data analysis using log linear models, this book uses multinomial probability models both in cross-sectional and longitudinal setups. A theoretical foundation is provided for the analysis of univariate multinomial responses, by developing models systematically for the cases with no covariates as well as categorical covariates, both in cross-sectional and longitudinal setups. In the longitudinal setup, both stationary and non-stationary covariates are considered. These models have also been extended to the bivariate multinomial setup along with suitable covariates. For the inferences, the book uses the generalized quasi-likelihood as well as the exact likelihood approaches.

Introduction.- Overview of Regression Models for Cross-sectional Univariate Categorical Data.- Regression Models for Univariate Longitudinal Stationary Categorical Data.- Regression Models for Univariate Longitudinal Non-stationary Categorical Data.- Multinomial Models for Cross-sectional Bivariate Categorical Data.- Multinomial Models for Longitudinal Bivariate Categorical Data.- Index.