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AN INDUCTIVE APPROACH TO DETERMINING CAUSALITY IN COMPARATIVE POLITICS: A FUZZY SET ALTERNATIVE

机译:一种确定比较政治因果关系的归纳方法:一种模糊集替代方案

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摘要

Political science typically tests hypotheses by demonstrating correlations between variables. The most commonly employed method for doing so is regression analysis. The approach is essentially crisp, which carries with it a number of questionable assumptions about the data. Political phenomena such as democracy or stability often involve measuring the degree to which a particular observation demonstrates the qualities of the category. A fuzzy set approach better captures the inherent ambiguity in classifying our observations relative to such categories. However, the attempt to establish correlations between fuzzy sets in the social sciences has been plagued by the priority ranking issue. We illustrate the potential that Jeffrey's Rule has to overcome this difficulty.
机译:政治科学通常通过证明变量之间的相关性来检验假设。最常用的方法是回归分析。该方法本质上是简洁的,它带有许多关于数据的可疑假设。诸如民主或稳定之类的政治现象通常涉及对特定观察结果证明该类别质量的程度进行测量。模糊集方法可以更好地捕获相对于此类类别的观察结果分类中的固有歧义。但是,在社会科学中建立模糊集之间的相关性的尝试受到优先级排序问题的困扰。我们说明了杰弗里法则必须克服这一困难的潜力。

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