The SAGE Handbook of Regression Analysis and Causal Inference

Introduction – Christof Wolf and Henning Best PART I: ESTIMATION AND INFERENCE Estimation Techniques: Ordinary least squares and maximum likelihood – Martin Elff Bayesian Estimation of Regression Models – Susumu Shikano PART II: REGRESSION ANALYSIS FOR CROSS-SECTIONS Linear Regression – Christof Wolf and Henning Best Regression Analysis: Assumptions and Diagnostics – Bart Meuleman, Geert Loosveldt and Viktor Emonds Non-Linear and Non-Additive Effects in Linear Regression – Henning Lohmann The Multilevel Regression Model – Joop Hox and Leoniek Wijngaards-de Meij Logistic Regression – Henning Best and 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 and Patricia A. Berglund PART III: CAUSAL INFERENCE AND ANALYSIS OF LONGITUDINAL DATA Matching Estimators for Treatment Effects – Markus Gangl Instrumental Variables Regression – Christopher Muller, Christopher Winship and Stephen L. Morgan Regression Discontinuity Designs in Social Sciences – David S. Lee and Thomas Lemieux Fixed-effects Panel Regression – Josef Bruderl and Volker Ludwig Event History Analysis – Hans-Peter Blossfeld and Gwendoline J. Blossfeld Time-Series Cross-Section – Jessica Fortin-Rittberger