KU Leuven, University of Leuven.
Columbia University.
Perspect Psychol Sci. 2016 Sep;11(5):702-712. doi: 10.1177/1745691616658637.
Empirical research inevitably includes constructing a data set by processing raw data into a form ready for statistical analysis. Data processing often involves choices among several reasonable options for excluding, transforming, and coding data. We suggest that instead of performing only one analysis, researchers could perform a multiverse analysis, which involves performing all analyses across the whole set of alternatively processed data sets corresponding to a large set of reasonable scenarios. Using an example focusing on the effect of fertility on religiosity and political attitudes, we show that analyzing a single data set can be misleading and propose a multiverse analysis as an alternative practice. A multiverse analysis offers an idea of how much the conclusions change because of arbitrary choices in data construction and gives pointers as to which choices are most consequential in the fragility of the result.
实证研究不可避免地包括通过将原始数据处理成准备进行统计分析的形式来构建数据集。数据处理通常涉及在排除、转换和编码数据的几种合理选择之间进行选择。我们建议研究人员不要只进行一次分析，而是可以进行多元宇宙分析，这涉及到对与大量合理场景相对应的整个替代处理数据集系列进行所有分析。我们使用一个关注生育对宗教信仰和政治态度影响的例子，表明分析单个数据集可能会产生误导，并提出多元宇宙分析作为替代实践。多元宇宙分析提供了一个关于结论因数据构建中的任意选择而发生变化的程度的概念，并指出了在结果的脆弱性方面哪些选择是最关键的。
