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SAGE Handbook of Regression Analysis and Causal Inference

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'The editors of the new SAGE Handbook of Regression Analysis and Causal Inference have assembled a wide-ranging, high-quality, an... Lire la suite
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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.



Résumé
'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 ChicagoEdited 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.

Contenu

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

Informations sur le produit

Titre: SAGE Handbook of Regression Analysis and Causal Inference
Éditeur:
Code EAN: 9781473908352
ISBN: 978-1-4739-0835-2
Protection contre la copie numérique: Adobe DRM
Format: eBook (pdf)
Editeur: Sage Publications
Genre: Sciences politiques
nombre de pages: 424
Parution: 20.12.2013
Année: 2014
Sous-titre: Englisch