Table of Contents

Loglinear Marginal Models.- Nonloglinear Marginal Models.- Marginal Analysis of Longitudinal Data.- Causal Analyses: Structural Equation Models and (Quasi-)Experimental Designs.- Marginal modeling with latent variables.- Conclusions, Extensions, and Applications.

Reviews

From the reviews:"In conclusion, this book is a very well written book about an important topic in categorical data analysis. The methodology is presented with clear examples from applied research. The book is intended for applied researchers but I think that psychometricians, biometricians, and alike can learn a lot from the material in this book. Therefore, the book certainly deserves a spot on the categorical data analysis bookshelf for anyone that regularly encounters categorical data!" (Mark de Rooij, Vereniging voor Ordinatie en Classificatie (VOR), Nieuwsbrief No. 43, Oktober 2009)“This is a well-written book on marginal models for categorical data using maximum likelihood(ML) methods… The authors successfully show the methodology of marginal modeling in an intuitive way for social and behavioral scientists…This book would be a fantastic reference for statisticians interested in learning more about repeated categorical response data analysis and marginal modeling beyond the familiar GEE procedure.” (Biometrics)“Social and behavioral scientists with good background in social science statistics and applied statisticians. The authors write that this book has been written with applied researchers in mind. … it serves the purpose well. … The book explains in pedagogical way the types of research questions for which marginal modeling is useful, and provides a detailed description of how to apply marginal models for a great diversity of research questions in several fields, i.e., social, economic and behavioral sciences.” (Lasse Koskinen, International Statistical Review, Vol. 78 (1), 2010)