Graphical Descriptives: A Way to Improve Data Transparency and Methodological Rigor in Psychology

Louis Tay, Scott Parrigon, Qiming Huang, James M. LeBreton

Research output: Contribution to journalArticlepeer-review

31 Scopus citations


Several calls have recently been issued to the social sciences for enhanced transparency of research processes and enhanced rigor in the methodological treatment of data and data analytics. We propose the use of graphical descriptives (GDs) as one mechanism for responding to both of these calls. GDs provide a way to visually examine data. They serve as quick and efficient tools for checking data distributions, variable relations, and the potential appropriateness of different statistical analyses (e.g., do data meet the minimum assumptions for a particular analytic method). Consequently, we believe that GDs can promote increased transparency in the journal review process, encourage best practices for data analysis, and promote a more inductive approach to understanding psychological data. We illustrate the value of potentially including GDs as a step in the peer-review process and provide a user-friendly online resource ( for researchers interested in including data visualizations in their research. We conclude with suggestions on how GDs can be expanded and developed to enhance transparency.

Original languageEnglish (US)
Pages (from-to)692-701
Number of pages10
JournalPerspectives on Psychological Science
Issue number5
StatePublished - Sep 1 2016

All Science Journal Classification (ASJC) codes

  • General Psychology


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