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Identifying context and cause in small-N settings: a comparative multilevel analysis

机译:在小N环境中识别上下文和原因:比较多层次分析

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Qualitative small-N comparisons face the challenge to detect context-bound causality under conditions of limited empirical diversity. Rather than treating context as a causal factor, we test the usefulness of the novel method of comparative multilevel analysis (CMA) to identify and understand the role of context as a contingent necessary condition that enables a causal relationship to unfold. Combining CMA with pairwise comparisons, we assess how organ donation policies in Switzerland and Spain affect relatives' refusal rates in a small-N setting exhibiting multiple contextual levels. To tackle limited diversity systematically, we suggest to refine the CMA methodology by accounting for several contexts and referring to higher-order constructs. Applying CMA with these refinements, we find voluntary information measures only affect refusal rates in contexts of a credible state explicitly supporting organ donation. The fact that CMA can easily be combined with other analytical and conceptual approaches makes it an effective technique to identify contextual effects in small-N research.
机译:小样本N定性比较面临着在有限的经验多样性条件下检测上下文相关因果关系的挑战。而不是将上下文视为因果因素,我们测试了比较多层次分析(CMA)新颖方法的有效性,该方法可识别和理解上下文作为使因果关系得以展开的不可或缺的必要条件的作用。将CMA与成对比较相结合,我们评估了瑞士和西班牙的器官捐赠政策如何在具有多个背景水平的小N环境下影响亲戚的拒绝率。为了系统地解决有限的多样性,我们建议通过考虑多种情​​况并引用高阶构造来完善CMA方法。通过对CMA进行这些改进,我们发现自愿信息措施仅在明确支持器官捐赠的可信国家的情况下影响拒绝率。 CMA可以轻松地与其他分析和概念方法结合使用,这一事实使其成为识别小型N研究中情境影响的有效技术。

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