Examining the Reproducibility of 6 Published Studies in Public Health Services and Systems Research. Issue 2 (March 2019)

• Record Type: Journal Article
• Authors: Harris, Jenine K.; Wondmeneh, Sarah B.; Zhao, Yiqiang; Leider, Jonathon P.
• Is Part Of: Journal of public health management and practice. Volume 25:Issue 2(2019)
• Publication Date: 2019-03
• Subjects: open science -- public health services and systems research -- reproducibility; Public health administration -- United States -- Periodicals; 253.6
• DOI: 10.1097/PHH.0000000000000694
• Languages: English
• ISSNs: 1078-4659

Abstract

Abstract : Objective: Research replication, or repeating a study de novo, is the scientific standard for building evidence and identifying spurious results. While replication is ideal, it is often expensive and time consuming. Reproducibility, or reanalysis of data to verify published findings, is one proposed minimum alternative standard. While a lack of research reproducibility has been identified as a serious and prevalent problem in biomedical research and a few other fields, little work has been done to examine the reproducibility of public health research. We examined reproducibility in 6 studies from the public health services and systems research subfield of public health research. Design: Following the methods described in each of the 6 papers, we computed the descriptive and inferential statistics for each study. We compared our results with the original study results and examined the percentage differences in descriptive statistics and differences in effect size, significance, and precision of inferential statistics. All project work was completed in 2017. Results: We found consistency between original and reproduced results for each paper in at least 1 of the 4 areas examined. However, we also found some inconsistency. We identified incorrect transcription of results and omitting detail about data management and analyses as the primary contributors to the inconsistencies. Recommendations: Increasing reproducibility, or reanalysis of data to verify published results, can improve the quality of science. Researchers, journals, employers, and funders can all play a role in improving the reproducibility of science through several strategies including publishing data and statistical code, using guidelines to write clear and complete methods sections, conducting reproducibility reviews, and incentivizing reproducible science. Abstract : Supplemental Digital Content is Available in the Text.