For example: according to Janz (2016), “working transparently involves maintaining detailed logs of data collection and variable transformations as well as of the analysis itself” ( JANZ, 2016, p. Regarding transparency, the terminology is also ambiguous. National Science Foundation (NSF) defines reproducibility as “the ability of a researcher to duplicate the results of a prior study using the same materials as were used by the original investigator” (NSF, 2015, p. They also propose a new lexicon for research reproducibility in three dimensions: methods, results and inferences. Goodman, Fanelli and Ioannidis (2016) argue that the current use of reproducible research was initially employed in computational science not for corroboration but transparency. The meaning of reproducibility is also mutable. Seawright and Collier (2004)argue that “two different research practices are both called replication: a narrow version, which involves reanalyzing the original data, and a broader version based on collecting and analyzing new data” ( SEAWRIGHT and COLLIER, 2004, p. According to Hamermesh (2007), replication has three perspectives: pure, statistical and scientific. (2011)ĭefine replication as “the confirmation of results and conclusions from one study obtained independently in another” ( JASNY et al., 2011, p. For example, King (1995)argues that “the replication standard holds that sufficient information exists with which to understand, evaluate, and build upon a prior work if a third party could replicate the results without any additional information from the author” ( KING, 1995, p. Scholars from different fields use these concepts without a common understanding of their definition (PATIL, PENG and LEEK, 2016, p. To make the problem even worse, there is no agreement as to what transparency, reproducibility and replication mean exactly ( JANZ, 2016). In short, both the natural and the social sciences are facing a reproducibility crisis ( FREESE and PETERSON, 2017 LEEK and PENG, 2015 MUNAFÒ et al., 2017). Similar trends are found in sociology ( LUCAS et al., 2013), economics ( CHANG and LI, 2015 DEWALD et al., 1986 KRAWCZYK and REUBEN, 2012), international relations ( GLEDITSCH and JANZ, 2016), psychology ( NOSEK et al., 2015), medicine ( SAVAGE and VICKERS, 2009), biostatistics ( LEEK and PENG, 2015) and genetics ( MARKOWETZ, 2015). In political science, Key (2016)reports that 67.6% of all articles published in the American Political Science Review (APSR) between 20 do not provide replication materials. The scholarly interest in transparency, reproducibility and replication has been rising recently, with compelling evidence that many scientific studies have failed to be replicated (GOODMAN, FANELLI and IOANNIDIS, 2016).
Keywords: TransparencyTransparency,reproducibilityreproducibility,replicationreplication. With this paper, we hope to foster transparency within the political science scholarly community. In addition, we review the most recent computational tools to work reproducibly. enabling the continuity of academic work 05. enhancing the reviewers’ ability to provide better evaluations 04. making the writing and publishing of papers more efficient 03. production of trustworthy empirical results, by preventing intentional frauds and avoiding honest mistakes 02.
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We argue that sharing replication materials, which include full disclosure of the methods used to collect and analyze data, the public availability of raw and manipulated data, in addition to computational scripts, may generate the following positive outcomes: 01. In this paper, we present seven reasons why journals and authors should implement - transparent guidelines. Abstract: Despite a widespread agreement on the importance of transparency in science, a growing body of evidence suggests that both the natural and the social sciences are facing a reproducibility crisis.
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