Rethinking Social Inquiry: Diverse Tools, Shared Standards

Henry E. Brady, David Collier

Rowman & Littlefield, 2004 - Social Science - 362 pages

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 debate in journal articles, conference panels, and books. Rethinking Social Inquiry is a major new effort by a broad range of leading scholars to offer a cohesive set of reflections on Designing Social Inquiry's quest for common standards drawn from quantitative methodology. While vigorously agreeing to the need for common standards, the essays in Rethinking Social Inquiry argue forcefully that these standards must be drawn from exemplary qualitative research as well as the best quantitative studies. The essays make the case that good social science requires a set of diverse tools for inquiry. Published in cooperation with the Berkeley Public Policy Press.

Contents

• Refocusing the Discussion of Methodology: 3
• The Debate on Designing Social Inquiry: 5
• Where Do We Go from Here?: 6
• Tools and Standards: 7
• Toward an Alternative View of Methodology: 10
• Overview of the Chapters: 14
• Critiques of the Quantitative Template: 15
• Qualitative Tools: 16
• Linking the Quantitative and Qualitative Traditions: 18
• Diverse Tools Shared Standards: 19
• The Quest for Standards King Keohane and Verbas Designing Social Inquiry: 21
• Scientific Research Inference and Assumptions: 22
• Inference: 23
• Causal Inference: 25
• Quantitative Tools and Analytic Goals: 26
• Assumptions: 28
• Causal Homogeneity: 29
• Independence of Observations: 30
• Conditional Independence: 31
• Summarizing DSIs Framework: 36
• A Defining the Research Problem: 37
• B Specifying the Theory: 38
• D Descriptive Inference: 40
• E Causal Inference: 42
• F Further Testing and Reformulating the Theory: 44
• II Areas of Divergence: 46
• Critiques of the Quantitative Template: 51
• Doing Good and Doing Better How Far Does the Quantitative Template Get Us?: 53
• Descending from the Rhetorical Heights: 56
• Measurement: 62
• Conclusion: 66
• Some Unfulfilled Promises of Quantitative Imperialism: 69
• The Contribution and a Shortcoming: 70
• Omissions and an Agenda for Research: 71
• Measurement Error: 72
• Multiplying Observations: 73
• Conclusion: 74
• How Inference in the Social but Not the Physical Sciences Neglects Theoretical Anomaly: 75
• Problemation and Deductive Theorizing: 76
• Some Examples: 77
• Lessons: 82
• Claiming Too Much Warnings about Selection Bias: 85
• Do the Warnings Claim Too Much?: 86
• Why Is It an Issue?: 88
• An Example: 89
• Understanding Why Selection Bias Results from Truncation: 90
• Selection Bias in Qualitative Research: 92
• CrossCase Analysis and Selection Bias: 94
• WithinCase Analysis and Selection Bias: 95
• Evaluating the Causal Relationship: 96
• Atypical Cases and Overgeneralization: 97
• Stern Warnings about NoVariance Designs: 99
• Further Observations about CrossCase and WithinCase Comparison: 100
• Conclusion: 101
• Qualitative Tools: 103
• Tools for Qualitative Research: 105
• A Survey of Tools: 107
• Dilemmas of Increasing the Number of Observations: 112
• Measurement and Data Collection: 115
• Causal Assessment in CrossCase and WithinCase Designs: 116
• Theory Generation Reformulation and the Iterated Assessment of Hypotheses: 119
• Conclusion: 120
• Turning the Tables How CaseOriented Research Challenges VariableOriented Research: 123
• Constitution of Cases: 125
• Study of Uniform Outcomes: 128
• Definition of Negative Cases: 130
• Examination of Multiple and Conjunctural Causes: 133
• Treatment of Nonconforming Cases and Determinism: 135
• Conclusion: 138
• Case Studies and the Limits of the Quantitative Worldview: 139
• Philosophy of Science and the Logic of Research: 140
• DSI and the Popperian View of Theory: 142
• A Single Logic of Research: 143
• Is Inference Fundamentally Quantitative?: 144
• Making Inferences from One or a Few Cases: 146
• An Alternative Logic for Case Studies: 154
• Cognitive Mapping: 155
• Game Theory Applied to Empirical Situations: 156
• A Folk Bayesian Approach: 158
• Heuristics for Theory Construction: 162
• Case Selection Heuristics: 163
• Exploiting Feedback from Observation to Design: 164
• Conclusion: 166
• Linking the Quantitative and Qualitative Traditions: 169
• Bridging the QuantitativeQualitative Divide: 171
• Challenges of Combining Qualitative and Quantitative Data: 172
• Tools for Bridging the Divide: 173
• Systematic and Nonsystematic Variable Discrimination: 174
• Framing Qualitative Research within Quantitative Profiles: 175
• Putting Qualitative Flesh on Quantitative Bones: 176
• Sequencing Quantitative and Qualitative Research: 177
• Triangulation: 178
• Conclusion: 179
• The Importance of Research Design: 181
• What We Tried to Do: 182
• Our Alleged Errors of Omission: 186
• Distinguishing History from Social Science: 187
• The Perils of Avoiding Selection Bias: 188
• Distinguishing Descriptive Inference from Causal Inference: 189
• How to Identify a Dependent Variable: 190
• Triangular Conclusions: 191
• Diverse Tools Shared Standards: 193
• Critiques Responses and TradeOffs Drawing Together the Debate: 195
• Critiques and Statistical Responses: 196
• Doing Research That Is Important: 197
• Critique: 198
• Statistical Response: 199
• The Challenge of Promoting Creativity: 200
• Innovative Research TradeOffs and DSIs Framework: 201
• Conceptualization and Measurement: 202
• Critique: 203
• Statistical Response: 204
• Selection Bias: 209
• Critique: 210
• Statistical Response: 211
• Probabilistic versus Deterministic Models of Causation: 213
• Critique: 214
• Statistical Response: 216
• The Statistical Responses: 220
• TradeOffs in Research Design: 221
• TradeOffs in DSI: 224
• Conclusion: 226
• Sources of Leverage in Causal Inference Toward an Alternative View of Methodology: 229
• Revisiting Some Key Distinctions: 230
• Mainstream Quantitative Methods versus Statistical Theory: 233
• Determinate versus Indeterminate Research Designs: 236
• Data Mining versus Specification Searches: 238
• Conditional Independence or the Specification Assumption: 240
• Four Approaches to the Qualitative versus Quantitative Distinction: 244
• Level of Measurement: 245
• Statistical Tests: 248
• Drawing Together the Four Criteria: 249
• Cases versus Observations: 250
• DataSet Observations versus CausalProcess Observations: 252
• Examples of CausalProcess Observations: 256
• Implications of Contrasting Types of Observations: 258
• Qualitative versus Quantitative: 260
• Implications for Research Design: 262
• Missing Data: 263
• Drawing Together the Argument: 264
• Technification and the Quest for Shared Standards: 266
• DataSet Observations versus CausalProcess Observations The 2000 US Presidential Election: 267
• The Option of Regression Analysis: 268
• Turning to CausalProcess Observations: 269
• Where Did Lott Go Wrong?: 270
• Conclusion: 271
• Glossary: 273
• Bibliography: 315
• Subject Index: 339
• Name Index: 347
• Contributors: 355
• Acknowledgment of Permission to Reprint Copyrighted Material: 361

Bibliographic information

• Title: Rethinking Social Inquiry: Diverse Tools, Shared Standards; G - Reference, Information and Interdisciplinary Subjects Series
• Editors: Henry E. Brady, David Collier
• Publisher: Rowman & Littlefield, 2004
• ISBN: 0742511251, 9780742511255
• Length: 362 pages
• Subjects: Social Science / Methodology; Social Science / Research; Social Science / Sociology / General