By I. Rohlfing
A dialogue of the case research technique which develops an integrative framework for causal inference in small-n study. This framework is utilized to analyze layout initiatives corresponding to case choice and approach tracing. The ebook offers the fundamentals, cutting-edge and arguments for bettering the case examine strategy and empirical small-n examine.
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Extra resources for Case Studies and Causal Inference: An Integrative Framework
An illustrative set relation is the observation that whenever there is a high GDP per capita (invariant condition), the illiteracy rate of a country is low (invariant outcome). 4). It is occasionally argued that the search for set relations does not allow one to say anything about causal effects (for example, King and Powell 2008, 16). This assertion is based on a covariational, that is, difference-making understanding of causation that does not fit squarely with set-relational thinking. Put simply and assuming that the cause is dichotomous, the causal effect of a variable is the difference between the scores of the outcome when the Case, Case Study, and Causation 33 variable takes different scores (Morgan and Winship 2007).
Much as in quantitative research (Schrodt 2010), 18 Case Studies and Causal Inference frequentist research makes inferences about the likelihood of evidence taking a hypothesis for granted, whereas Bayesian analyzes generate inferences about hypothesis in light of empirical evidence. Third, the different perspectives that frequentism and Bayesianism take go along with different degrees of confidence in the generated inferences. Frequentists make an either/or inference, meaning that one either concludes that the evidence supports the hypothesis or not.
The understanding of what similar cases are, and thus the shape of the population, may change throughout the research process (Ragin 2000, chap. 2). Such changes can be due to the need to redefine concepts (Adcock and Collier 2001), to add or remove scope conditions (Walker and Cohen 1985), or to exclude individual cases from the population (Mahoney and Goertz 2004). For these reasons, one should always stay open-minded to a change of the population and an understanding of causal homogeneity during and after the empirical analysis.