poorly specified variables, or the choice of lenient significance levels. These are attributes of good and poor research and not of a dichotomy between two different types of research. (For more information, see Yin, 6e, Chapter 1, section on in Case Studies as a “Variations Research Method.)” 19. Replication, Not Sampling, for Multiple-Case Studies; see logic” in the “replication 6e glossary. The replication logic for case studies borrows directly from the same logic in doing experiments. Neither the number of cases in a case study or the number of experiments in an experimental study are likely to be large. However, if the cases (or experiments) corroborate one another, the multiple-case findings can be considered to be more robust. In contrast, a sampling logic is used when a study is mainly interested in having its findings pertain to a larger population or universe. The sample is chosen according to pre-identified representational criteria—and the sample must be sufficiently large (usually many data points) to satisfy a statistical power analysis. (For more information, see Yin, 6e, Chapter 2, subsection on Not Sampling “Replication, Logic, for Multiple-Case Studies.”) 20. Rival Explanations; see explanations” in the 6e glossary. “rival No concept is more helpful in conducting any kind of research than the concept of rival explanations. Yet, existing social science texts rarely point to the importance of this concept, much less give guidance on how to articulate or investigate such rivals. The most common rival explanation has been the null hypothesis. A null hypothesis is simply that, in a statistical study, the observed effect or outcome occurred by chance alone and not because of any hypothesized intervention. In doing case studies, the more important rivals are those that point substantively to other plausible conditions or forces that might explain what really happened in a case. When you have identified rival explanations in this sense, you can collect data to examine the competing explanations and compare the results through a pattern-matching process. The better and more numerous the rivals that can be investigated in this manner, the stronger your case study will be—whether you conclude that the evidence supports your original explanation or one of the rivals. (For more information, see Yin, 6e, Chapter 5, subsection on Plausible Rival “Examining Explanations.”)
poorly specified variables, or the choice of lenient significance levels. These are attributes of good and poor research and not of a dichotomy between two different types of research. (For more information, see Yin, 6e, Chapter 1, section on “Variations in Case Studies as a Research Method.)” 19. Replication, Not Sampling, for Multiple-Case Studies; see “replication logic” in the 6e glossary. The replication logic for case studies borrows directly from the same logic in doing experiments. Neither the number of cases in a case study or the number of experiments in an experimental study are likely to be large. However, if the cases (or experiments) corroborate one another, the multiple-case findings can be considered to be more robust. In contrast, a sampling logic is used when a study is mainly interested in having its findings pertain to a larger population or universe. The sample is chosen according to pre-identified representational criteria—and the sample must be sufficiently large (usually many data points) to satisfy a statistical power analysis. (For more information, see Yin, 6e, Chapter 2, subsection on “Replication, Not Sampling Logic, for Multiple-Case Studies.”) 20. Rival Explanations; see “rival explanations” in the 6e glossary. No concept is more helpful in conducting any kind of research than the concept of rival explanations. Yet, existing social science texts rarely point to the importance of this concept, much less give guidance on how to articulate or investigate such rivals. The most common rival explanation has been the null hypothesis. A null hypothesis is simply that, in a statistical study, the observed effect or outcome occurred by chance alone and not because of any hypothesized intervention. In doing case studies, the more important rivals are those that point substantively to other plausible conditions or forces that might explain what really happened in a case. When you have identified rival explanations in this sense, you can collect data to examine the competing explanations and compare the results through a pattern-matching process. The better and more numerous the rivals that can be investigated in this manner, the stronger your case study will be—whether you conclude that the evidence supports your original explanation or one of the rivals. (For more information, see Yin, 6e, Chapter 5, subsection on “Examining Plausible Rival Explanations.”)