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      Increasing Transparency Through a Multiverse Analysis.

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          Abstract

          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.

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          Author and article information

          Journal
          Perspect Psychol Sci
          Perspectives on psychological science : a journal of the Association for Psychological Science
          1745-6924
          1745-6916
          Sep 2016
          : 11
          : 5
          Affiliations
          [1 ] KU Leuven, University of Leuven.
          [2 ] Columbia University.
          [3 ] KU Leuven, University of Leuven wolf.vanpaemel@ppw.kuleuven.be.
          Article
          11/5/702
          10.1177/1745691616658637
          27694465
          facf6e36-8bdf-4ed0-8148-4914cf00dd72
          © The Author(s) 2016.
          History

          arbitrary choices,data processing,good research practices,multiverse analysis,selective reporting,transparency

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