Designing Social Inquiry

Designing Social Inquiry: Scientific Inference in Qualitative Research

Gary King
Robert O. Keohane
Sidney Verba
Copyright Date: 1994
Edition: STU - Student edition
Pages: 300
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  • Book Info
    Designing Social Inquiry
    Book Description:

    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.

    eISBN: 978-1-4008-2121-1
    Subjects: Sociology

Table of Contents

  1. Front Matter
    (pp. i-iv)
  2. Table of Contents
    (pp. v-viii)
  3. Preface
    (pp. ix-2)
    Gary King, Robert O. Keohanne and Sidney Verba
  4. CHAPTER 1 The Science in Social Science
    (pp. 3-33)

    This book is about research in the social sciences. Our goal is practical: designing research that will produce valid inferences about social and political life. We focus on political science, but our argument applies to other disciplines such as sociology, anthropology, history, economics, and psychology and to nondisciplinary areas of study such as legal evidence, education research, and clinical reasoning.

    This is neither a work in the philosophy of the social sciences nor a guide to specific research tasks such as the design of surveys, conduct of field work, or analysis of statistical data. Rather, this is a book about...

  5. CHAPTER 2 Descriptive Inference
    (pp. 34-74)

    Social science research, whether quantitative or qualitative, involves the dual goals of describing and explaining. Some scholars set out to describe the world; others to explain. Each is essential. We cannot construct meaningful causal explanations without good description; description, in turn, loses most of its interest unless linked to some causal relationships. Description often comes first; it is hard to develop explanations before we know something about the world and what needs to be explained on the basis of what characteristics. But the relationship between description and explanation is interactive. Sometimes our explanations lead us to look for descriptions of...

  6. CHAPTER 3 Causality and Causal Inference
    (pp. 75-114)

    We have discussed two stages of social science research: summarizing historical detail (section 2.5) and making descriptive inferences by partitioning the world into systematic and nonsystematic components (section 2.6). Many students of social and political phenomena would stop at this point, eschewing causal statements and asking their selected and well-ordered facts to “speak for themselves.”

    Like historians, social scientists need to summarize historical detail and to make descriptive inferences. For some social scientific purposes, however, analysis is incomplete without causal inference. That is, just as causal inference is impossible without good descriptive inference, descriptive inference alone is often unsatisfying and...

  7. CHAPTER 4 Determining What to Observe
    (pp. 115-149)

    Up to this point, we have presented our view of the standards of scientific inference as they apply to both qualitative and quantitative research (chapter 1), defined descriptive inference (chapter 2), and clarified our notion of causality and causal inference (chapter 3). We now proceed to consider specific practical problems of qualitative research design. In this and the next two chapters, we will use many examples, both drawn from the literature and constructed hypothetically, to illustrate our points. This chapter focuses on how we should select cases, or observations, for our analysis. Much turns on these decisions, since poor case...

  8. CHAPTER 5 Understanding What to Avoid
    (pp. 150-207)

    In chapter 4, we discussed how to construct a study with a determinate research design in which observation selection procedures make valid inferences possible. Carrying out this task successfully is necessary but not sufficient if we are to make valid inferences: analytical errors later in the research process can destroy the good work we have done earlier. In this chapter, we discuss how, once we have selected observations for analysis, we can understand sources of inefficiency and bias and reduce them to manageable proportions. We will then consider how we can control the research in such a way as to...

  9. CHAPTER 6 Increasing the Number of Observations
    (pp. 208-230)

    In this book we have stressed the crucial importance of maximizing leverage over research problems. The primary way to do this is to find as many observable implications of your theory as possible and to make observations of those implications. As we have emphasized, what may appear to be a single-case study, or a study of only a few cases, may indeed contain many potential observations, at different levels of analysis, that are relevant to the theory being evaluated. By increasing the number of observations, even without more data collection, the researcher can often transform an intractable problem that has...

  10. References
    (pp. 231-238)
  11. Index
    (pp. 239-247)