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Data Inference in Observational Settings [Multiple-component retail product]

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  • Formaat: Multiple-component retail product, 1648 pages, kõrgus x laius: 234x156 mm, kaal: 3100 g, 4 Items, Contains 4 hardbacks
  • Sari: Sage Benchmarks in Social Research Methods
  • Ilmumisaeg: 12-Dec-2013
  • Kirjastus: Sage Publications Ltd
  • ISBN-10: 1446266508
  • ISBN-13: 9781446266502
Teised raamatud teemal:
  • Formaat: Multiple-component retail product, 1648 pages, kõrgus x laius: 234x156 mm, kaal: 3100 g, 4 Items, Contains 4 hardbacks
  • Sari: Sage Benchmarks in Social Research Methods
  • Ilmumisaeg: 12-Dec-2013
  • Kirjastus: Sage Publications Ltd
  • ISBN-10: 1446266508
  • ISBN-13: 9781446266502
Teised raamatud teemal:
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: BackgroundVolume Two: Analytical TechniquesVolume Three: Temporal RelationsVolume Four: Experimental Analogues 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

Arvustused

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. Its 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 researchers 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 *

Appendix of Sources xi
Editor's Introduction: Data Inference in Observational Settings xxi
Peter Davis
Volume I Background
A Causal Inference from Observational Data
1 Estimating Causal Effects of Treatments in Randomized and Nonrandomized Studies
3(20)
Donald B. Rubin
2 Statistics and Causal Inference
23(36)
Paul W. Holland
3 Misunderstandings between Experimentalists and Observationalists about Causal Inference
59(28)
Kosuke Imai
Gary King
Elizabeth A. Stuart
4 The Estimation of Causal Effects from Observational Data
87(46)
Christopher Winship
Stephen L. Morgan
5 Causal Inference in Sociological Research
133(38)
Markus Gangl
B Potential Outcomes and Counterfactuals
6 On the Application of Probability Theory to Agricultural Experiments: Essay on Principles - Section 9
171(14)
Jerzy Splawa-Neyman
7 Comment: Neyman (1923) and Causal Inference in Experiments and Observational Studies
185(16)
Donald B. Rubin
8 Causal Inference Using Potential Outcomes: Design, Modeling, Decisions
201(22)
Donald B. Rubin
9 Counterfactuals and Hypothesis Testing in Political Science
223(26)
James D. Fearon
10 Does Marriage Reduce Crime? A Counterfactual Approach to Within-Individual Causal Effects
249(40)
Robert J. Sampson
John H. Laub
Christopher Wimer
C Programme and Policy Evaluation
11 Reforms as Experiments
289(34)
Donald T. Campbell
12 Evaluating the Econometric Evaluations of Training Programs with Experimental Data
323(22)
Robert J. LaLonde
13 Choosing among Alternative Nonexperimental Methods for Estimating the Impact of Social Programs: The Case of Manpower Training
345(26)
James J. Heckman
V. Joseph Hotz
14 Estimating the Effects of Potential Public Health Interventions on Population Disease Burden: A Step-by-Step Illustration of Causal Inference Methods
371(16)
Jennifer Ahern
Alan Hubbard
Sandro Galea
15 The Credibility Revolution in Empirical Economics: How Better Research Design Is Taking the Con out of Econometrics
387
Joshua D. Angrist
Jorn-Steffen Pischke
Volume II Analytical Techniques
D Matching Methods
16 The Effectiveness of Adjustment by Subclassification in Removing Bias in Observational Studies
3(16)
W.G. Cochran
17 Reducing Bias in Observational Studies Using Subclassification on the Propensity Score
19(16)
Paul R. Rosenbaum
Donald B. Rubin
18 Matching with Multiple Controls to Estimate Treatment Effects in Observational Studies
35(24)
Herbert L. Smith
19 Matching Estimators of Causal Effects: Prospects and Pitfalls in Theory and Practice
59(50)
Stephen L. Morgan
David J. Harding
20 Matching Methods for Causal Inference: A Review and a Look Forward
109(38)
Elizabeth A. Stuart
E Propensity Scoring
21 The Central Role of the Propensity Score in Observational Studies for Causal Effects
147(18)
Paul R. Rosenbaum
Donald B. Rubin
22 Propensity Score-Matching Methods for Nonexperimental Causal Studies
165(24)
Rajeev H. Dehejia
Sadek Wahba
23 Too Much Ado about Propensity Score Models? Comparing Methods of Propensity Score Matching
189(16)
Onur Baser
24 A Comparison of the Ability of Different Propensity Score Models to Balance Measured Variables between Treated and Untreated Subjects: A Monte Carlo Study
205(22)
Peter C. Austin
Paul Grootendorst
Geoffrey M. Anderson
25 Selection Bias in Web Surveys and the Use of Propensity Scores
227(26)
Matthias Schonlau
Arthur van Soest
Arie Kapteyn
Mick Couper
F Causal Diagrams
26 Correlation and Causation
253(32)
Sewall Wright
27 Structural Equation Methods in the Social Sciences
285(26)
Arthur S. Goldberger
28 Causal Diagrams for Empirical Research
311(24)
Judea Pearl
29 From Causal Diagrams to Birth Weight-Specific Curves of Infant Mortality
335(8)
Sonia Hernandez-Diaz
Allen J. Wilcox
Enrique F. Schisterman
Miguel A. Hernan
30 Neighborhood Effects in Temporal Perspective: The Impact of Long-term Exposure to Concentrated Disadvantage on High School Graduation
343
Geoffrey T. Wodtke
David J. Harding
Felix Elwert
Volume III Temporal Relations
G Panel Studies
31 Causal Inference from Panel Data
3(24)
David R. Heise
32 Using Panel Data to Estimate the Effects of Events
27(20)
Paul D. Allison
33 The Impact of Incarceration on Wage Mobility and Inequality
47(26)
Bruce Western
34 Panel Models in Sociological Research: Theory into Practice
73
Charles N. Halaby
35 Correlation or Causation? Income Inequality and Infant Mortality in Fixed Effects Models in the Period 1960--2008 in 34 OECD Countries
111(18)
Mauricio Avendano
H Family Studies
36 Sibling Models and Data in Economics: Beginnings of a Survey
129(26)
Zvi Griliches
37 Fraternal Resemblance in Educational Attainment and Occupational Status
155(22)
Robert M. Hauser
Peter A. Mossel
38 Is Biology Destiny? Birth Weight and Life Chances
177(12)
Dalton Conley
Neil G. Bennett
39 Schooling or Social Origin? The Bias in the Effect of Educational Attainment on Social Orientations
189(22)
Inge Sieben
Paul M. de Graaf
40 Social Science Methods for Twins Data: Integrating Causality, Endowments, and Heritability
211(66)
Hans-Peter Kohler
Jere R. Behrman
Jason Schnittker
I Instrumental Variables
41 Problems with Instrumental Variables Estimation When the Correlation between the Instruments and the Endogenous Explanatory Variable Is Weak
277(18)
John Bound
David A. Jaeger
Regina M. Baker
42 Identification of Causal Effects Using Instrumental Variables
295(24)
Joshua D. Angrist
Guido W. Imbens
Donald B. Rubin
43 The Colonial Origins of Comparative Development: An Empirical Investigation
319(44)
Daron Acemoglu
Simon Johnson
James A. Robinson
44 A Genetic Instrumental Variables Analysis of the Effects of Prenatal Smoking on Birth Weight: Evidence from Two Samples
363(30)
George L. Wehby
Jason M. Fletcher
Steven F. Lehrer
Lina M. Moreno
Jeffrey C. Murray
Allen Wilcox
Rolv T. Lie
45 Instrumental Variables in Sociology and the Social Sciences
393
Kenneth A. Bollen
Volume IV Experimental Analogues
J The Experimental Paradigm
46 Causal Inference from Randomized Trials in Social Epidemiology
3(22)
Jay S. Kaufman
Sol Kaufman
Charles Poole
47 What Do Randomized Studies of Housing Mobility Demonstrate?: Causal Inference in the Face of Interference
25(20)
Michael E. Sobel
48 Three Conditions under Which Experiments and Observational Studies Produce Comparable Causal Estimates: New Findings from Within-Study Comparisons
45(34)
Thomas D. Cook
William R. Shadish
Vivian C. Wong
49 The Impact of Elections on Cooperation: Evidence from a Lab-in-the-Field Experiment in Uganda
79(34)
Guy Grossman
Delia Baldassarri
50 Neighborhood Effects on the Long-term Well-being of Low-Income Adults
113(16)
Jens Ludwig
Greg J. Duncan
Lisa A. Gennetian
Lawrence F. Katz
Ronald C. Kessler
Jeffrey R. Kling
Lisa Sanbonmatsu
K Regression Discontinuity
51 Regression-Discontinuity Analysis: An Alternative to the Ex Post Facto Experiment
129(10)
Donald L. Thistlethwaite
Donald T. Campbell
52 Assignment to Treatment Group on the Basis of a Covariate
139(18)
Donald B. Rubin
53 Capitalizing on Nonrandom Assignment to Treatments: A Regression-Discontinuity Evaluation of a Crime-Control Program
157(14)
Richard A. Berk
David Rauma
54 Identification and Estimation of Local Average Treatment Effects
171(10)
Guido W. Imbens
Joshua D. Angrist
55 An Evaluation of California's Inmate Classification System Using a Generalized Regression Discontinuity Design
181(20)
Richard A. Berk
Jan de Leeuw
L Quasi-Experiments and Natural Experiments
56 Minimum Wages and Employment: A Case Study of the Fast-Food Industry in New Jersey and Pennsylvania
201(28)
David Card
Alan B. Krueger
57 Natural and Quasi-Experiments in Economics
229(24)
Bruce D. Meyer
58 How Much Should We Trust Differences-in-Differences Estimates?
253(24)
Marianne Bertrand
Esther Duflo
Sendhil Mullainathan
59 A Natural Experiment on Residential Change and Recidivism: Lessons from Hurricane Katrina
277(30)
David S. Kirk
60 Effects of Prenatal Poverty on Infant Health: State Earned Income Tax Credits and Birth Weight
307
Kate W. Strully
David H. Rehkopf
Ziming Xuan
Peter Davis is Director of the COMPASS Research Centre and Professor of Sociology at the University of Auckland, with cross-appointments in the School of Population Health and in the Department of Statistics, also at the University of Auckland. Previously he served as Professor of Public Health at the University of Otagos Christchurch School of Medicine. Davis specialises in medical sociology, and has achieved international recognition in his field, having worked as a consultant for the World Health Organisation. His main interests are in research methods, social structures, and policy, particularly health policy and health services. He has collaborated with colleagues in health research and in social statistics on a number of major surveys since the 1970s. He was Senior Editor (Health Policy) at the international journal, Social Science and Medicine, until 2012.