Description
‘The editors of the new SAGE Handbook of Regression Analysis and Causal Inference have assembled a wide-ranging, high-quality, and timely collection of articles on topics of central importance to quantitative social research, many written by leaders in the field. Everyone engaged in statistical analysis of social-science data will find something of interest in this book.’ – John Fox, Professor, Department of Sociology, McMaster University ‘The authors do a great job in explaining the various statistical methods in a clear and simple way – focussing on fundamental understanding, interpretation of results, and practical application – yet being precise in their exposition.’ – Ben Jann, Executive Director, Institute of Sociology, University of Bern ‘Best and Wolf have put together a powerful collection, especially valuable in its separate discussions of uses for both cross-sectional and panel data analysis.’ -Tom Smith, Senior Fellow, NORC, University of Chicago 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. Each Part starts with a non-mathematical introduction to the method covered in that section, giving readers a basic knowledge of the method’s logic, scope and unique features. Next, the mathematical and statistical basis of each method is presented along with advanced aspects. Using real-world data from the European Social Survey (ESS) and the Socio-Economic Panel (GSOEP), the book provides a comprehensive discussion of each method’s application, making this an ideal text for PhD students and researchers embarking on their own data analysis. 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
