Seven Reasons Why: A User’s Guide to Transparency and Reproducibility

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. In this paper, we present seven reasons why journals and authors should implement-transparent guidelines. 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. production of trustworthy empirical results, by preventing intentional frauds and avoiding honest mistakes; 02. making the writing and publishing of papers more efficient; 03. enhancing the reviewers ' ability to provide better evaluations; 04. enabling the continuity of academic work; 05. developing scientific reputation; 06. helping to learn data analysis; and 07. increasing the impact of scholarly work. In addition, we review the most recent computational tools to work reproducibly. With this paper, we hope to foster transparency within the political science scholarly community.

Transparency and Replication in Brazilian Political Science: A First Look

“…Around the world, the credibility crisis in science poses a challenge to scholars' efforts to advance knowledge both in the natural and the social disciplines. As Figueiredo et al (2019) discussed, norms of transparency and replicability are becoming part of standard scientific practice in the United States, Western Europe, and other regions. The scientific community cannot neglect credibility issues anymore.…”

“…Research transparency is one of these new practices. By research transparency we mean providing all the resources needed for other scholars to replicate one's findings, including source materials, computer code, and other relevant resources (King, Keohane and Verba, 1994;Figueiredo Filho et al, 2019;Bakken, 2019). Transparency permits studies' replication and reproducibility, which are key factors for research's credibility (Janz, 2016).…”

The forensics of fraud: Evidence from the 2018 Brazilian presidential election

“…All statistical estimations were implemented using R Statistical, version 4.0.5. Following scientific best practices [ 24 ], the replication materials, including data and computational scripts, are available at the Open Science Framework (OSF).…”

Why Quantitative Variables Should Not Be Recoded as Categorical

“…Besides, we employ basic simulation to show how dichotomization generates inefficiency and bias. To increase transparency [7] [8] [9], we report all computational scripts used to generate statistical analyses.…”