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The SAGE Handbook of Regression Analysis and Causal Inference

The SAGE Handbook of Regression Analysis and Causal Inference

First Edition
Edited by:

November 2014 | 424 pages | SAGE Publications Ltd

'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.  

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

Sample Materials & Chapters

Ch. 4 Linear Regression

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