By I. Rohlfing

A dialogue of the case research procedure which develops an integrative framework for causal inference in small-n examine. This framework is utilized to investigate layout projects resembling case choice and technique tracing. The e-book offers the fundamentals, state of the art and arguments for bettering the case learn strategy and empirical small-n learn.

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However, principled arguments and an empirical example underscore an important similarity between frequentism and Introduction 19 Bayesianism, namely that, ceteris paribus, causal inferences are always more credible the larger the number of confirming observations. 24 In addition to a comparison of frequentism and Bayesianism, Chapter 8 includes a full exposition of Bayesian case in a simple and slightly formalized perspective. A formalized treatment of Bayesian small-n research sheds new light on some long-standing and intuitively plausible recommendations.

In response to this criticism, it is explained how the transfer of causes into scope conditions can be used to delineate intelligible and small populations that allow for generalization. Moreover, I introduce the strategy of layered generalization for the systematic and stepwise expansion of populations in case study research. This strategy transforms actual scope conditions into potential causes in order to discern their relevance for the outcome under scrutiny. Chapter 10 concludes with a larger picture of the previous chapters and highlights the fact that the case study method can contribute to causal inferences on causal effects and mechanisms if it is implemented in a disciplined and reflective manner.

Consequently, one would conclude that misfit and adaptation costs are the cause of the delay. A Bayesian case study would use the same evidence – internal documents and officials referring to adaptation costs as the cause of a delayed transposition – for the generation of a different causal inference. As explained above, the Bayesian case study would center on a case for which it is unlikely that misfit accounts for a delayed transposition. If we gather evidence now that supports the misfit hypothesis, we can be much more confident in the accuracy of the misfit hypothesis than before the empirical analysis because the proposition could master a relatively tough test (this argument will be qualified in Chapter 8).

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