Increasing Transparency Through a Multiverse Analysis.
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.
KU Leuven, University of Leuven.
Sara
S
Steegen
KU Leuven, University of Leuven.
Francis
F
Tuerlinckx
Columbia University.
Andrew
A
Gelman
KU Leuven, University of Leuven wolf.vanpaemel@ppw.kuleuven.be.
Wolf
W
Vanpaemel
D001290:Attitude; D002755:Choice Behavior; D003627:Data Interpretation, Statistical; D005260:Female; D005298:Fertility; D006801:Humans; D019451:Information Management / Q000379:methods; D017533:Marital Status; D011057:Politics; D012067:Religion; D012107:Research Design
arbitrary choices; data processing; good research practices; multiverse analysis; selective reporting; transparency