By I. Rohlfing
A dialogue of the case research strategy which develops an integrative framework for causal inference in small-n study. This framework is utilized to investigate layout initiatives reminiscent of case choice and approach tracing. The e-book offers the fundamentals, cutting-edge and arguments for making improvements to the case research procedure and empirical small-n study.
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Additional resources for Case Studies and Causal Inference: An Integrative Framework
5 The exclusive use of statistical tools for causal inference is what I take as the defining feature of a quantitative case study. It is not the use of numbers and figures that qualifies a case study as quantitative (Maxwell 2010, 476–7) but the exclusive analysis of data with statistical means. 6 In contrast to genuine quantitative case studies, the pool of collected evidence is more diverse and includes primary and secondary sources as well as interviews yielding observations that cannot be made subject to a quantitative analysis (Collier et al.
The remaining four sections examine the relationship between a large property space and a small number of cases. With regard to the latter aspect of the indeterminacy problem, an often recommended but limited means to improve comparisons is an increase in the number of cases. The third instrument pertains to the level of measurement aggregation of the causes and outcome. The level of measurement aggregation asks whether causes and/or the outcome are measured binarily or multicategorically. It is shown that binary measurement is inferentially advantageous because the property space is reduced.
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.