Data Inference in Observational Settings - Sage Benchmarks in Social Research Methods (Hardback)
  • Data Inference in Observational Settings - Sage Benchmarks in Social Research Methods (Hardback)

Data Inference in Observational Settings - Sage Benchmarks in Social Research Methods (Hardback)

Hardback 1648 Pages / Published: 12/12/2013
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Most social research is carried out in observational settings; that is, most social researchers collect information in the "real world" trying to do as little possible to alter the circumstances of study. However, there is a fundamental problem with this kind of research, in that it is very hard to draw "causal" conclusions, because of the complexity and obduracy of social reality. This is not just a problem for social scientists interested in policy or social action. It applies across the board more generally because it becomes difficult to know, without the conditions for credible inference, what conclusions can be drawn from any piece of empirical research that aspires to be anything more than descriptive of social phenomena.

This four-volume set of readings introduces the reader to the advances that have been made in trying to help social researchers draw more credible inferences from investigations carried out in observational settings. Drawing from a variety of sources - from logicians and philosophers, to applied statisticians, computer scientists and econometricians, to epidemiologists and social researchers - this collection provides an invaluable resource for scholars in the field.

Volume One: Background

Volume Two: Analytical Techniques

Volume Three: Temporal Relations

Volume Four: Experimental Analogues

Publisher: SAGE Publications Ltd
ISBN: 9781446266502
Number of pages: 1648
Weight: 3100 g
Dimensions: 234 x 156 x 122 mm


While causal thinking is at the heart of social science research and explanation, too little rigorous attention is paid by researchers as how to strengthen claims of causality. This comprehensive collection draws together some of the best papers that point to the challenges of establishing causality and provide ways of addressing many of these challenges. It provides the resources to help both researchers and students address the question of causality much more systematically and convincingly than is often the case.

-- Professor David de Vaus

An excellent collection of seminal papers summarizing the background to, and the state of the art for, methods which are becoming central to the conduct of epidemiology and other areas of health and social research in the 21st century.

-- Dr. Neil Pearce
These are the canonical papers on causal inference, organized for the first time into one useful handbook. It's a must-have for all researchers in the social sciences. I shall be recommending it to all my students. -- Ichiro Kawachi, M.D., Ph.D.

These volumes bring together a core set of important papers on the critical topic of causal inference and will prove to be an extremely useful source for recommended core reading for researchers and students alike.

-- Professor Nick Wareham

This four-volume reader is the best place to start if you are interested in an overview of how to make cause inference from observational data. The selection concisely covers a vast literature that has rapidly developed over a period of several decades. You will read seminal methodological contributions, excellent review articles and important applications in these volumes. Instructors in the social sciences may use this reader for a graduate level methodology course. Researchers will find it a useful reference on their bookshelves. Policy analysts will enter a whole new world of dialogue if they become familiar with the rationale and techniques summarized in this reader.

-- Assistant Professor Jui-Chung Allen Li
For Chinese researchers and students, I believe a comprehensive collection of rigorous papers on causality will enhance the claims of study findings for a rapidly changing society. The handbook will provide a useful tool for researchers and students to meet the challenges of addressing causal relationships. -- Professor Xiulan Zhang

In social science research, oftentimes, the researcher's ultimate goal is to be able to make causal inference statements about what would contribute to socially significant outcomes. Unfortunately, not being able to implement true experimental design in most social science research situations makes such causal inference risky and full of pitfalls, as it can become very difficult to rule out rival hypotheses or explanations. This collection of seminal papers on issues related to making causal inferences provides a "must read" for social science researchers, green hand or experienced alike, who desire to avoid numerous pitfalls in the process of making causal inferences in social science research.

-- Xitao Fan, Ph.D. * Chair Professor & Dean, Faculty of Education, University of Macau, Macao, China *

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