Correlation Versus Interchangeability: The Limited Robustness of Empirical Findings on Democracy Using Highly Correlated Data Sets
Correlation Versus Interchangeability: The Limited Robustness of Empirical Findings on Democracy Using Highly Correlated Data Sets
复制标题
相关性与可互换性：使用高度相关数据集的民主实证研究结果的稳健性有限
DOI：
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发表时间：
2003
影响因子：
5.4
通讯作者：
Claudiu Daniel Tufis
中科院分区：
文献类型：
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作者：
Gretchen Casper;Claudiu Daniel Tufis
本文表明，高度相关的度量可以产生不同的结果。我们从文献中确定了一个民主化模型，并从1951年到1992年在120多个国家进行了测试。然后，我们检查结果在民主、时期和发展水平方面是否稳健。研究结果表明，措施确实很重要：尽管一些研究结果是可靠的，但大多数都不是。这在一定程度上解释了为什么关于民主的辩论一直在继续，而不是已经解决。更重要的是，它强调需要更谨慎地使用措施和进一步的测试，以增加对研究结果的信心。比较政治学的学者越来越倾向于使用他人收集的数据集进行大n统计分析。就像在任何领域一样，我们展示了他们在为他们的研究选择最合适的测量方法时必须如何谨慎，而不是假设任何相关的测量方法都可以。
This article shows that highly correlated measures can produce different results. We identify a democratization model from the literature and test it in more than 120 countries from 1951 to 1992. Then, we check whether the results are robust regarding measures of democracy, time periods, and levels of development. The findings show that measures do matter: Whereas some of the findings are robust, most of them are not. This explains, in part, why the debates on democracy have continued rather than been resolved. More important, it underscores the need for more careful use of measures and further testing to increase confidence in the findings. Scholars in comparative politics are drawn increasingly to large-N statistical analyses, often using data sets collected by others. As in any field, we show how they must be careful in choosing the most appropriate measures for their studies, without assuming that any correlated measure will do.