The SAGE Handbook of Regression Analysis and Causal Inference

The SAGE Handbook of Regression Analysis and Causal Inference

By: Christof Wolf (editor), Henning Best (editor)Hardback

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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.

Contents

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

Product Details

  • ISBN13: 9781446252444
  • Format: Hardback
  • Number Of Pages: 424
  • ID: 9781446252444
  • weight: 930
  • ISBN10: 1446252442

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