Includes bibliographical references (p. [231]-238) and index.

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
vi · Contents : 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
Contents · vii 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