- Subject index
‘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 ...
Chapter 1: Introduction
In recent years, the social sciences have made tremendous progress in quantitative methodology and data analysis. The classical linear model, while still remaining an important foundation for more advanced methods, has been increasingly complemented by specialized techniques. Major improvements include the widespread use of non-linear models, advances in multilevel modeling and Bayesian estimation, the diffusion of longitudinal analyses and, more recently, the focus on novel methods for causal inference.
The interested reader can chose from a number of excellent textbooks on a wide range of topics: starting from general econometrics books such as Wooldridge (2009, 2010) or Greene (2012), ranging over volumes on regression and Bayesian methods (Gelman et al., 2003; Fox, 2008; Gelman and Hill, 2007), multilevel modeling (Hox, 2010), non-linear models ...