Advice for Improving the Reproducibility of Data Extraction in Meta-Analysis

Autor/inn/en	Ivimey-Cook, Edward R.; Noble, Daniel W. A.; Nakagawa, Shinichi; Lajeunesse, Marc J.; Pick, Joel L.
Titel	Advice for Improving the Reproducibility of Data Extraction in Meta-Analysis
Quelle	In: Research Synthesis Methods, 14 (2023) 6, S.911-915 (5 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Ivimey-Cook, Edward R.) ORCID (Noble, Daniel W. A.) ORCID (Nakagawa, Shinichi) ORCID (Lajeunesse, Marc J.) ORCID (Pick, Joel L.)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1759-2879
DOI	10.1002/jrsm.1663
Schlagwörter	Replication (Evaluation); Data Collection; Meta Analysis; Computer Software; Statistical Analysis + Suchen Sie Ihr Suchwort? Data capture; Datensammlung; Meta-analysis; Metaanalyse; Statistische Analyse
Abstract	Extracting data from studies is the norm in meta-analyses, enabling researchers to generate effect sizes when raw data are otherwise not available. While there has been a general push for increased reproducibility in meta-analysis, the transparency and reproducibility of the data extraction phase is still lagging behind. Unfortunately, there is little guidance of how to make this process more transparent and shareable. To address this, we provide several steps to help increase the reproducibility of data extraction in meta-analysis. We also provide suggestions of R software that can further help with reproducible data policies: the "shinyDigitise" and "juicr" packages. Adopting the guiding principles listed here and using the appropriate software will provide a more transparent form of data extraction in meta-analyses. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01