Handbook of Causal Analysis for Social Research 在线电子书 pdf 下载 txt下载 epub 下载 mobi 下载 2024

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Morgan, Stephen L 编作者

Springer

译者

2013-5-4 出版日期

450 页数

USD 349.00 价格

Hardcover

Handbooks of Sociology and Social Research 丛书系列

9789400760936 图书编码

Handbook of Causal Analysis for Social Research 在线电子书图书标签: Methodology 社会学政治学社会科学社会方法论方法因果推理

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Handbook of Causal Analysis for Social Research 在线电子书 epub 下载 mobi 下载 pdf 下载 txt 下载 2024

Handbook of Causal Analysis for Social Research 在线电子书 pdf 下载 txt下载 epub 下载 mobi 下载 2024

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因果关系几乎是所有学科研究的终极对象，在自然科学中严苛控制各类变量的实验室更易作出因果解释，但社会科学则往往无法严控所有变量，使因果结论疲软无力甚至在媒体传播中与数据呈现出来的相关性混淆，这在很大程度上造成一些学科出现重复性危机。或许有人认为社科根本不是科学，然而这并不是社科的原罪，反体现出自然科学那套研究体系工具的局限性。此书展现了另一个方面：研究者们使用数学工具在因果分析领域所取得的丰硕成果，而这些与通常意义上的统计方法不同，需要额外学习。因果分析本身还在持续发展中，很多问题仍没有最终答案，你甚至能在本书看到不同篇章的专家作者相互质疑对方观点的内容。专业、全面、深入原本值五星，但阅读难度颇高扣一星，很多章节的叙述方式完全没有通俗易懂深入浅的意思，能简单说的话偏要刻意难，初学者还是再见吧。

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Handbook of Causal Analysis for Social Research 在线电子书著者简介

Stephen L. Morgan is the Jan Rock Zubrow '77 Professor in the Social Sciences at Cornell University, Ithaca, NY, USA.

Handbook of Causal Analysis for Social Research 在线电子书图书目录

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.-
· · · · · · (收起)

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Handbook of Causal Analysis for Social Research 在线电子书图书描述

Causal inference is a fundamental goal of social research, and it has been a topic of methodological research for decades. The evaluation of social science theory cannot proceed without assessing the sizes of entailed cause-effect relationships. Policy research cannot be conducted without estimating the impacts that follow from policy interventions. Unfortunately, for most social science research, controlled experimentation is not possible. And, when experimentation is feasible, it is often only possible in artificial contexts and for subjects who are not the representative of the target populations for inference. Tremendous progress has been made in the past 15 years in the causal analysis of non-experimental data, also known as observational data. The proposed handbook aims to explain this progress and then demonstrate how to use state-of-the-art methods for causal analysis in basic and applied empirical scholarship. The methods involve defining causal contrasts using counterfactual definitions and then estimating differences across individuals while maintaining clear assumptions about these contrasts. This approach allows for advanced forms of regression and multivariate case-matching, as well longitudinal differencing techniques, and instrumental variable estimation based on the occurrence of natural experiments. In the tradition that will be explicated in this handbook, substantial attention will also be devoted to representing underlying assumptions using causal graphs.

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