Increasing Transparency Through a Multiverse Analysis

Steegen, Sara

doi:10.1177/1745691616658637

2016-09-29

750

DOI：10.1177/1745691616658637

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摘要

En 中文

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.

Keyword:

multiverse analysis

arbitrary choices

data processing

good research practices

transparency

selective reporting

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期刊

Perspectives on Psychological Science

IF:

8.4

论文数:

1.5K

被引数:

1.8W

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Columbia University

学者数:

7.1W

论文数: 6.4W

被引数: 263

KU Leuven

学者数:

5.7W

论文数: 5.2W

被引数: 8.1W

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