Rethinking Social Inquiry,9780742511262

Rethinking Social Inquiry

by
ISBN13: 9780742511262
ISBN10: 074251126X
Format: Paperback
Pub. Date: 2004-09-01
Publisher(s): Rowman & Littlefield Pub Inc
List Price: $44.00

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Summary

When it was first published, Designing Social Inquiry, by political scientists Gary King, Robert Keohane, and Sidney Verba, at once struck chords of controversy. As it became one of the best-selling methodology books in memory, it continued to spark

Table of Contents

List of Figures and Tables xv
Preface xvii
I. Introduction 1(50)
1. Refocusing the Discussion of Methodology
3(18)
Henry E. Brady, David Collier, and Jason Seawright
Mainstream Quantitative Methods, Qualitative Methods, and Statistical Theory
3(2)
The Debate on Designing Social Inquiry
5(2)
DSI's Contribution
5(1)
Where Do We Go from Here?
6(1)
Tools and Standards
7(3)
Toward an Alternative View of Methodology
10(4)
Overview of the Chapters
14(8)
Introduction
14(1)
Critiques of the Quantitative Template
15(1)
Qualitative Tools
16(2)
Linking the Quantitative and Qualitative Traditions
18(1)
Diverse Tools, Shared Standards
19(2)
2. The Quest for Standards: King, Keohane, and Verba's Designing Social Inquiry
21(30)
David Collier, Jason Seawright, and Gerardo L. Munck
Scientific Research, Inference, and Assumptions
22(14)
Scientific Research
22(1)
Inference
23(1)
Descriptive Inference
23(1)
Causal Inference
25(1)
Making Inferences: Quantitative Tools and Analytic Goals
26(2)
Assumptions
28(1)
Causal Homogeneity
29(1)
Independence of Observations
30(1)
Conditional Independence
31(5)
Guidelines: Summarizing DST's Framework
36(8)
A. Defining the Research Problem
37(1)
B. Specifying the Theory
38(1)
C. Selecting Cases and Observations
38(2)
D. Descriptive Inference
40(2)
E. Causal Inference
42(2)
F. Further Testing and Reformulating the Theory
44(1)
Anticipating the Discussion of DSI's Framework
44(9)
I. Areas of Convergence
44(2)
II. Areas of Divergence
46(5)
II. Critiques of the Quantitative Template 51(52)
3. Doing Good and Doing Better: How Far Does the Quantitative Template Get Us?
53(16)
Henry E. Brady
Theology versus Homiletics
53(3)
Descending from the Rhetorical Heights
56(10)
Explanation and Causality
56(6)
Measurement
62(4)
Conclusion
66(3)
4. Some Unfulfilled Promises of Quantitative Imperialism
69(6)
Larry M. Bartels
The Contribution and a Shortcoming
70(1)
Omissions and an Agenda for Research
71(3)
Uncertainty
71(1)
Qualitative Evidence
71(1)
Measurement Error
72(1)
Multiplying Observations
73(1)
Conclusion
74(1)
5. How Inference in the Social (but Not the Physical) Sciences Neglects Theoretical Anomaly
75(10)
Ronald Rogowski
Problemation and Deductive Theorizing
76(1)
Theory and Anomaly: Some Examples
77(5)
Lessons
82(3)
6. Claiming Too Much: Warnings about Selection Bias
85(18)
David Collier, James Mahoney, and Jason Seawright
Do the Warnings Claim Too Much?
86(2)
Selecting Extreme Values on the Dependent Variable: Why Is It an Issue?
88(4)
An Example
89(1)
Understanding Why Selection Bias Results from Truncation
90(2)
Selection Bias in Qualitative Research
92(7)
Cross-Case Analysis and Selection Bias
94(1)
Within-Case Analysis and Selection Bias
95(1)
Evaluating the Causal Relationship
96(1)
Atypical Cases and Overgeneralization
97(2)
Stern Warnings about No-Variance Designs
99(1)
Further Observations about Cross-Case and Within-Case Comparison
100(1)
Conclusion
101(2)
III. Qualitative Tools 103(66)
7. Tools for Qualitative Research
105(18)
Gerardo L. Munck
Qualitative Methods: A Survey of Tools
107(13)
Defining the Universe of Cases: Context, Typologies, and Process Tracing
107(5)
Case Selection: Dilemmas of Increasing the Number of Observations
112(3)
Measurement and Data Collection
115(1)
Causal Assessment in Cross-Case and Within-Case Designs
116(3)
Beyond Strict Hypothesis Testing: Theory Generation, Reformulation, and the Iterated Assessment of Hypotheses
119(1)
Conclusion
120(3)
8. Turning the Tables: How Case-Oriented Research Challenges Variable-Oriented Research
123(16)
Charles C. Ragin
Constitution of Cases
125(3)
Study of Uniform Outcomes
128(2)
Definition of Negative Cases
130(3)
Examination of Multiple and Conjunctural Causes
133(2)
Treatment of Nonconforming Cases and "Determinism"
135(3)
Conclusion
138(1)
9. Case Studies and the Limits of the Quantitative Worldview
139(30)
Timothy J. McKeown
Philosophy of Science and the Logic of Research
140(4)
DSI's Philosophy of Science
140(2)
DSI and the Popperian View of Theory
142(1)
A Single Logic of Research
143(1)
Is Inference Fundamentally Quantitative?
144(2)
Making Inferences from One or a Few Cases
146(8)
Toward a Methodology of Intensive Research: An Alternative Logic for Case Studies
154(12)
Understanding Existing Research
154(1)
Cognitive Mapping
155(1)
Game Theory Applied to Empirical Situations
156(2)
A "Folk Bayesian" Approach
158(4)
Heuristics for Theory Construction
162(1)
Case Selection Heuristics
163(1)
Thought Experiments and Counterfactuals
163(1)
Exploiting Feedback from Observation to Design
164(1)
Identifying Causal Processes Rather than Testing
164(2)
Conclusion
166(3)
IV. Linking the Quantitative and Qualitative Traditions 169(1)
10. Bridging the Quantitative-Qualitative Divide
171(22)
Sidney Tarrow
Challenges of Combining Qualitative and Quantitative Data
172(1)
Tools for Bridging the Divide
173(1)
Tracing Processes to Interpret Decisions
173(1)
Systematic and Nonsystematic Variable Discrimination
174(1)
Framing Qualitative Research within Quantitative Profiles
175(1)
Putting Qualitative Flesh on Quantitative Bones
176(1)
Sequencing Quantitative and Qualitative Research
177(1)
Triangulation
178(1)
Conclusion
179(2)
11. The Importance of Research Design
181(1)
Gary King, Robert O. Keohane, and Sidney Verba
What We Tried to Do
182(4)
Our Alleged Errors of Omission
186(2)
Science as a Collective Enterprise
186(1)
Lijphart: The Case Study That Broke the Pluralist Camel's Back
186(1)
Allen: Distinguishing History from Social Science
187(1)
The Perils of Avoiding Selection Bias
188(3)
Katzenstein: Distinguishing Descriptive Inference from Causal Inference
189(1)
Bates: How to Identify a Dependent Variable
190(1)
Triangular Conclusions
191(2)
V. Diverse Tools, Shared Standards 193(1)
12. Critiques, Responses, and Trade-Offs: Drawing Together the Debate
195(72)
David Collier, Henry E. Brady, and Jason Seawright
Critiques and Statistical Responses
196(25)
Doing Research That Is Important
197(1)
Critique
198(1)
Statistical Response
199(1)
The Challenge of Promoting Creativity
200(1)
Innovative Research, Trade-Offs, and DSI's Framework
201(1)
Conceptualization and Measurement
202(1)
Critique
203(1)
Statistical Response
204(5)
Selection Bias
209(1)
Critique
210(1)
Statistical Response
211(2)
Probabilistic versus Deterministic Models of Causation
213(1)
Critique
214(1)
Statistical Response
216(4)
The Statistical Responses
220(1)
Trade-Offs in Research Design
221(5)
Trade-Offs, Goals, and Tools
221(3)
Trade-Offs in DSI
224(1)
Placing Trade-Offs at the Center of Attention
224(2)
Conclusion
226(3)
13. Sources of Leverage in Causal Inference: Toward an Alternative View of Methodology
229(1)
David Collier, Henry E. Brady, and Jason Seawright
Revisiting Some Key Distinctions
230(14)
Experiments, Quasi-Experiments, Observational Studies, and Inferential Monsters
230(3)
Mainstream Quantitative Methods versus Statistical Theory
233(3)
Determinate versus Indeterminate Research Designs
236(2)
Data Mining versus Specification Searches
238(2)
Conditional Independence or the Specification Assumption
240(4)
Four Approaches to the Qualitative versus Quantitative Distinction
244(6)
Level of Measurement
245(1)
Size of the N
245(3)
Statistical Tests
248(1)
Thick versus Thin Analysis
248(1)
Drawing Together the Four Criteria
249(1)
Cases versus Observations
250(2)
Cases
250(1)
Observations
250(2)
Data-Set Observations versus Causal-Process Observations
252(12)
Examples of Causal-Process Observations
256(2)
Implications of Contrasting Types of Observations
258(1)
Qualitative versus Quantitative
260(1)
Adding Observations and Adding Variables: Consequences for the N, Degrees of Freedom, and Inferential Leverage
260(2)
Implications for Research Design
262(1)
Missing Data
263(1)
Standard Quantitative Tools versus Careful Analysis of Causal-Process Observations
263(1)
Conclusion: Drawing Together the Argument
264(2)
Balancing Methodological Priorities: Technification and the Quest for Shared Standards
266(1)
Appendix Data-Set Observations versus Causal-Process Observations: The 2000 U.S. Presidential Election 267(1)
Henry E. Brady
The Option of Regression Analysis
268(1)
Turning to Causal-Process Observations
269(1)
Where Did Lott Go Wrong?
270(1)
Conclusion
271(2)
Glossary 273(1)
Jason Seawright and David Collier
Bibliography 315(24)
Subject Index 339(8)
Name Index 347(8)
Contributors 355(6)
Acknowledgment of Permission to Reprint Copyrighted Material 361

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