Handbook of Causal Analysis for Social Research

• Corporate Author: SpringerLink (Online service)
• Other Authors: Morgan, Stephen L. (Editor)
• Format: Electronic
• Language: English
• Published: Dordrecht : Springer Netherlands : Imprint: Springer, 2013.
• Series: Handbooks of Sociology and Social Research,
• Subjects: Social sciences.; Statistics.; Social sciences > Methodology.; Social Sciences.; Sociology, general.; Methodology of the Social Sciences.; Statistics for Social Science, Behavorial Science, Education, Public Policy, and Law.
• Online Access: https://ezaccess.library.uitm.edu.my/login?url=http://dx.doi.org/10.1007/978-94-007-6094-3

What constitutes a causal explanation, and must an explanation be causal?�What warrants a causal inference, as opposed to a descriptive regularity?�What techniques are available to detect when causal effects are present, and when can these techniques be used to identify the relative importance of th...

Table of Contents:

• Preface
• Chapter 1. Introduction; Stephen L. Morgan
• Part I. Background�and Approaches to Analysis
• Chapter�2. A History of Causal Analysis in the Social Sciences; Sondra N. Barringer, Erin Leahey and Scott R. Eliason
• Chapter 3. Types of Causes; Jeremy Freese and J. Alex Kevern
• Part II. Design and Modeling Choices
• Chapter 4. Research Design:�Toward a Realistic Role for Causal Analysis;�Herbert L.�Smith
• Chapter�5. Causal Models and Counterfactuals; James Mahoney, Gary Goertz and Charles C. Ragin
• Chapter 6. Mixed Models and Counterfactuals; David J. Harding and Kristin S. Seefeldt
• Part III. Beyond Conventional Regression Models
• Chapter 7.�Fixed Effects, Random Effects, and Hybrid Models for Causal Analysis; Glenn�Firebaugh, Cody Warner, and Michael Massoglia
• Chapter 8. Heteroscedastic Regression Models for the Systematic Analysis of Residual Variance; Hui Zheng, Yang Yang and Kenneth C. Land
• Chapter 9. Group Differences in Generalized Linear Models; Tim F. Liao
• Chapter 10. Counterfactual Causal Analysis and Non-Linear Probability Models; Richard Breen and Kristian Bernt Karlson
• Chapter 11. Causal Effect Heterogeneity; Jennie E. Brand and Juli Simon Thomas
• Chapter12. New Perspectives on Causal Mediation Analysis; Xiaolu Wang and Michael E. Sobel
• Part IV. Systems and Causal Relationships
• Chapter 13. Graphical Causal Models; Felix Elwert
• Chapter 14. The Causal Implications of� Mechanistic Thinking: Identification Using Directed Acyclic Graphs (DAGs); Carly R. Knight and Christopher Winship
• Chapter 15. Eight Myths about Causality and Structural Equation�Models; Kenneth A. Bollen and Judea Pearl
• Part V. Influence and Interference
• Chapter 16. Heterogeneous Agents, Social Interactions, and Causal Inference; Guanglei Hong and Stephen W. Raudenbush
• Chapter 17. Social Networks and Causal Inference; Tyler J. VanderWeele and Weihua An
• Part VI.�Retreat From Effect Identification
• Chapter 18. Partial Identification and Sensitivity Analysis; Markus Gangl
• Chapter 19. What You can Learn from Wrong Causal Models; Richard Berk, Lawrence Brown, Edward George, Emil Pitkin, Mikhail Traskin, Kai Zhang and Linda Zhao.