Edited and written by a team of leading international social scientists, this Handbook provides a comprehensive introduction to multivariate methods. The Handbook focuses on regression analysis of cross-sectional and longitudinal data with an emphasis on causal analysis, thereby covering a large number of different techniques including selection models, complex samples, and regression discontinuities.

Table of Contents

• Contributors
• Preface
• Introduction - Christof Wolf, Henning Best
• Estimation and Inference
• Estimation Techniques: Ordinary Least Squares and Maximum Likelihood - Martin Elff
• Bayesian Estimation of Regression Models - Susumu Shikano
• Regression Analysis for Cross-Sections
• Linear Regression - Christof Wolf, Henning Best
• Regression Analysis: Assumptions and Diagnostics - Bart Meuleman, Geert Loosveldt, Viktor Emonds
• Non-Linear and Non-Additive Effects in Linear Regression - Henning Lohmann
• The Multilevel Regression Model - Joop Hox, Leoniek Wijngaards-de Meij
• Logistic Regression - Henning Best, Christof Wolf
• Regression Models for Nominal and Ordinal Outcomes - J. Scott Long
• Graphical Display of Regression Results - Gerrit Bauer
• Regression with Complex Samples - Steven G. Heeringa, Brady T. West, Patricia A. Berglund
• Causal Inference and Analysis of Longitudinal Data
• Matching Estimators for Treatment Effects - Markus Gangl
• Instrumental Variables Regression - Christopher Muller, Christopher Winship, Stephen L. Morgan
• Regression Discontinuity Designs in Social Sciences - David S. Lee, Thomas Lemieux
• Fixed-Effects Panel Regression - Josef Brüderl, Volker Ludwig
• Event History Analysis - Hans-Peter Blossfeld, Gwendolin J. Blossfeld
• Time-Series Cross-Section - Jessica Fortin-Rittberger
• Subject Index