Designing Social Inquiry: Scientific Inference in Qualitative Research

While heated arguments between practitioners of qualitative and quantitative research have begun to test the very integrity of the social sciences, this monograph outlines a unified approach to valid descriptive and causal inference in qualitative research, where numerical measurement is either impossible or undesirable. The book demonstrates that the same logic of inference underlies both good quantitative and qualitative research designs. Providing precepts intended to stimulate and discipline thought, the authors explore issues related to framing research questions, measuring the accuracy of data and the uncertainty of empirical inferences, discovering causal effects and generally improving qualitative research. Among the specific topics addressed are interpretation and inference, comparative case studies, the construction of causal theories, dependent and explanatory variables, the limits of random selection, selection bias, and errors in measurement. Mathematical notation is occasionally used to clarify concepts, but no prior knowledge of mathematics or statistics is assumed. The unified logic of inference that this book explicates should be useful to qualitative researchers i

Table of Contents:
Preface ix 1 The Science in Social Science 3 1.1 Introduction 3 1.1.1 Two Styles of Research, One Logic of Inference 3 1.1.2 Defining Scientific Research in Social Sciences 7 1.1.3 Science and Complexity 9 1.2 Major Components of Research Design 12 1.2.1 Improving Research Questions 14 1.2.2 Improving Theory 19 1.2.3 Improving Data Quality 23 1.2.4 Improving the Use of Existing Data 27 1.3 Themes of This Volume 28 1.3.1 Using Observable Implications to Connect Theory and Data 28 1.3.2 Maximizing Leverage 29 1.3.3 Reporting Uncertainty 31 1.3.4 Thinking like a Social Scientist: Skepticism and Rival Hypotheses 32 2 Descriptive Inference 34 2.1 General Knowledge and Particular Facts 35 2.1.1 "Interpretation" and Inference 36 2.1.2 "Uniqueness," Complexity, and Simplification 42 2.1.3 Comparative Case Studies 43 2.2 Inference: the Scientific Purpose of Data Collection 46 2.3 Formal Models of Qualitative Research 49 2.4 A Formal Model of Data Collection 51 2.5 Summarizing Historical Detail 53 2.6 Descriptive Inference 55 2.7 Criteria for Judging Descriptive Inferences 63 2.7.1 Unbiased Inferences 63 2.7.2 Efficiency 66 3 Causality and Causal Inference 75 3.1 Defining Causality 76 3.1.1 The Definition and a Quantitative Example 76 3.1.2 A Qualitative Example 82 3.2 Clarifying Alternative Definitions of Causality 85 3.2.1 "Causal Mechanisms" 85 3.2.2 "Multiple Causality" 87 3.2.3 "Symmetric" and "Asymmetric" Causality 89 3.3 Assumptions Required for Estimating Causal Effects 91 3.3.1 Unit Homogeneity 91 3.3.2 Conditional Independence 94 3.4 Criteria for Judging Causal Inferences 97 3.5 Rules for Constructing Causal Theories 99 3.5.1 Rule 1: Construct Falsifiable Theories 100 3.5.2 Rule 2: Build Theories That Are Internally Consistent 105 3.5.3 Rule 3: Select Dependent Variables Carefully 107 3.5.4 Rule 4: Maximize Concreteness 109 3.5.5 Rule 5: State Theories in as Encompassing Ways as Feasible 113 4 Determining What to Observe 115 4.1 Indeterminate Research Designs 118 4.1.1 More Inferences than Observations 119 4.1.2 Multicollinearity 122 4.2 The Limits of Random Selection 124 4.3 Selection Bias 128 4.3.1 Selection on the Dependent Variable 129 4.3.2 Selection on an Explanatory Variable 137 4.3.3 Other Types of Selection Bias 138 4.4 Intentional Selection of Observations 139 4.4.1 Selecting Observations on the Explanatory Variable 140 4.4.2 Selecting a Range of Values of the Dependent Variable 141 4.4.3 Selecting Observations on Both Explanatory and Dependent Variables 142 4.4.4 Selecting Observations So the Key Causal Variable Is Constant 146 4.4.5 Selecting Observations So the Dependent Variable Is Constant 147 4.5 Concluding Remarks 149 5 Understanding What to Avoid 150 5.1 Measurement Error 151 5.1.1 Systematic Measurement Error 155 5.1.2 Nonsystematic Measurement Error 157 5.2 Excluding Relevant Variables: Bias 168 5.2.1 Gauging the Bias from Omitted Variables 168 5.2.2 Examples of Omitted Variable Bias 176 5.3 Including Irrelevant Variables: Inefficiency 182 5.4 Endogeneity 185 5.4.1 Correcting Biased Inferences 187 5.4.2 Parsing the Dependent Variable 188 5.4.3 Transforming Endogeneity into an Omitted Variable Problem 189 5.4.4 Selecting Observations to Avoid Endogeneity 191 5.4.5 Parsing the Explanatory Variable 193 5.5 Assigning Values of the Explanatory Variable 196 5.6 Controlling the Research Situation 199 5.7 Concluding Remarks 206 6 Increasing the Number of Observations 208 6.1 Single-Observation Designs for Causal Inference 209 6.1.1 "Crucial" Case Studies 209 6.1.2 Reasoning by Analogy 212 6.2 How Many Observations Are Enough? 213 6.3 Making Many Observations from Few 217 6.3.1 Same Measures, New Units 219 6.3.2 Same Units, New Measures 223 6.3.3 New Measures, New Units 224 6.4 Concluding Remarks 229 References 231 Index 239

About the Author :
Gary King is Professor of Government and Director of the Harvard Data Center at Harvard University. Robert O. Keohane is Stanfield Professor of International Peace at Harvard University. Sidney Verba is Carl H. Pforzheimer University Professor and Director of the Harvard University Library.

Review :
"The book is marked by a very careful building up of all concepts; by clear, vivid writing; and by an excellent use of extended examples from the work of such scholars as Nina Halpern, Atul Kohli, and David Laiting."--Journal of Politics