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While heated arguments between practitioners of qualitative and quantitative research have begun to test the very integrity of the social sciences, Gary King, Robert Keohane, and Sidney Verba have produced a farsighted and timely book that promises to sharpen and strengthen a wide range of research performed in this field.

These leading scholars, each representing diverse academic traditions, have developed a unified approach to valid descriptive and causal inference in qualitative research, where numerical measurement is either impossible or undesirable. Their book demonstrates that the same logic of inference underlies both good quantitative and good qualitative research designs, and their approach applies equally to each. Providing precepts intended to stimulate and discipline thought, the authors explore issues related to framing research questions, measuring the accuracy of data and uncertainty of empirical inferences, discovering causal effects, and generally improving qualitative research. Among the specific topics they address are interpretation and inference, comparative case studies, constructing 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 will be enormously useful to qualitative researchers of all traditions and substantive fields.

At a moment when acute disagreement among scholars over the appropriateness of qualitative and quantitative research methods threatens to undermine the validity and coherence of the social sciences, Gary King, Robert Keohane, and Sidney Verba have written a timely and far-sighted book that develops a unified approach to valid descriptive and causal inference. They illuminate the logic of good quantitative and good qualitative research designs and demonstrate that the two do not fundamentally differ. Designing Social Inquiry focuses on improving qualitative research, where numerical measurement is either impossible or undesirable.

What are the right questions to ask? How should you define and make inferences about causal effects? How can you avoid bias? How many cases do you need, and how should they be selected? What are the consequences of unavoidable problems in qualitative research, such as measurement error, incomplete information, or omitted variables? What are proper ways to estimate and report the uncertainty of your conclusions? How would you know if you were wrong? Designing Social Inquiry focuses on research in political science, but the authors' analyses apply much more widely. A political scientist conducting a small number of intensive case studies of Eastern European states; a sociologist interested in discovering the causes of social revolution; an education scholar conducting in-depth interviews of teachers in face-to-face settings; an anthropologist participating in and observing a newly discovered subculture; a lawyer studying the deterrent effects of capital punishment - these, and many other scholars and professionals in the social sciences, will come to rely on Designing Social Inquiry as an incomparable source book on the logic and design of research. Mathematical notation is occasionally used to clarify concepts, but no prior knowledge of mathematics or statistics is assumed.

Chapter 1: The Science in Social Science
1.1. Introduction
    1.1.1. Two Styles of Research, One Logic of Inference
    1.1.2 Defining Scientific Research in Social Sciences
    1.1.3 Science and Complexity
1.2 Major Components of Research Design
    1.2.1 Improving Research Questions
    1.2.2.Improving Data Quality
    1.2.3Improving the Use of Existing Data
1.3 Themes of This Volume
    1.3.1 Using Observable Implications to Connect Theory and Data
    1.3.2 Maximizing Leverage
    1.3.3 Reporting Uncertainty
    1.3.4 Thinking lie a Social Scientist: Skepticism and Rival Hypotheses

Chapter 2: Descriptive Inference
2.1 General Knowledge and Particular Facts
    2.1.1 "Interpretation" and Inference
    2.1.2 "Uniqueness", Complexity, and Simplification
    2.1.3 Comparative Case Studies
2.2 Inference: The Scientific Purpose of Data Collection
2.3 Formal Models of Qualitative Research
2.4 A Formal Model of Data Collection
2.5 Summarizing Historical Details
2.6 Descriptive Inference
2.7 Criteria for Judging Descriptive Inferences
    2.7.1 Unbiased Inferences
    2.7.2 Efficiency

Chapter 3: Causality and Causal Inference
3.1 Defining Causality
    3.1.1. The Definition and a Quantitative Example
    3.1.2 A Qualitative Example 3.2 Clarifying Alternative Definitions of Causality
    3.2.1 "Causal Mechanisms"
    3.2.2 "Multiple Causality"
    3.2.3 "Symmetric" and "Asymmetric" Causality
3.3. Assumptions Required for Estimating Causal Effects
    3.3.1 Unit Homogeneity
    3.3.2 Conditional Independence 3.4 Criteria for Judging Causal Inferences
3.5 Rules for Constructing Causal Theories
    3.5.1 Rule 1: Construct Falsifiable Theories
    3.5.2 Rule 2: Build Theories That are Internally Consistent
    3.5.3 Rule 3: Select Dependent Variables Carefully
    3.5.4 Rule 4: Maximize Concreteness
    3.5.5 Rule 5: State Theories in an Encompassing Ways as Feasible

Chapter 4: Determining What to Observe
4.1 Indeterminate Research Designs
    4.1.1 More Inferences than Observations
    4.1.2 Multicollinearity
4.2 The Limits of Random Selection
4.3 Selection Bias
    4.3.1 Selection on the Dependent Variable
    4.3.2 Selection on the Explanatory Variable
    4.3.3 Other Types of selection Bias 4.4. Intentional Selection of Observations
    4.4.1 Selection Observations on the Explanatory Variable
    4.4.2 Selecting Observations on the Dependent Variable
    4.4.3 Selecting Observations on Both Explanatory and Dependent Variable
    4.4.4 Selecting Observations So the Key Causal Variable is Constant
    4.4.5 Selecting Observations So the Dependent Variable Is Constant
4.5 Concluding Remarks

Chapter 5: Understanding What to Avoid
5.1 Measurement Error
    5.1.1. Systematic Measurement Error
    5.1.2 Non-systematic Measurement Error 5.2 Excluding Relevant Variables: Bias
    5.2.1 Gauging the Bias from Omitted Variables
    5.2.2. Examples of Omitted Variable Bias
5.3 Including Irrelevant Variables: Inefficiency
5.4 Endogeneity
    5.4.1 Correcting Biased Inferences
    5.4.2 Parsing the Dependent Variable
    5.4.3 Transforming Endogeneity Into an Omitted Variable Problem
    5.4.44 Selecting Observations to Avoid Endogeneity
    5.4.4. Selecting Observations to Avoid Endogeneity
    5.4.4 Parsing the Explanatory Variable
5.5. Assigning Values of the Explanatory Variable
5.6 Controlling the Research Situation
5.7 Concluding Remark

Chapter 6: Increasing the Number of Observations
6.1 Single-Observation Designs for Causal Inference
    6.1.1 "Crucial" Case Studies
    6.1.2 Reasoning by Analogy
6.2 How Many Observations Are Enough?
6.3 Making Many Observations from Few
    6.3.1 Same Measures, New Units
    6.3.2 Same Units, New Measures
    6.3.3. New Measures, New Units
6.4 Concluding Remarks