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One data set, many analysts: Implications for practicing scientists

Researchers routinely face choices throughout the data analysis process. It is often opaque to readers how these choices are made, how they affect the findings, and whether or not data analysis results are unduly influenced by subjective decisions. This concern is spurring numerous investigations in...

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Detalles Bibliográficos
Autores principales: Kummerfeld, Erich, Jones, Galin L.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9971968/
https://www.ncbi.nlm.nih.gov/pubmed/36865366
http://dx.doi.org/10.3389/fpsyg.2023.1094150
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author Kummerfeld, Erich
Jones, Galin L.
author_facet Kummerfeld, Erich
Jones, Galin L.
author_sort Kummerfeld, Erich
collection PubMed
description Researchers routinely face choices throughout the data analysis process. It is often opaque to readers how these choices are made, how they affect the findings, and whether or not data analysis results are unduly influenced by subjective decisions. This concern is spurring numerous investigations into the variability of data analysis results. The findings demonstrate that different teams analyzing the same data may reach different conclusions. This is the “many-analysts” problem. Previous research on the many-analysts problem focused on demonstrating its existence, without identifying specific practices for solving it. We address this gap by identifying three pitfalls that have contributed to the variability observed in many-analysts publications and providing suggestions on how to avoid them.
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spelling pubmed-99719682023-03-01 One data set, many analysts: Implications for practicing scientists Kummerfeld, Erich Jones, Galin L. Front Psychol Psychology Researchers routinely face choices throughout the data analysis process. It is often opaque to readers how these choices are made, how they affect the findings, and whether or not data analysis results are unduly influenced by subjective decisions. This concern is spurring numerous investigations into the variability of data analysis results. The findings demonstrate that different teams analyzing the same data may reach different conclusions. This is the “many-analysts” problem. Previous research on the many-analysts problem focused on demonstrating its existence, without identifying specific practices for solving it. We address this gap by identifying three pitfalls that have contributed to the variability observed in many-analysts publications and providing suggestions on how to avoid them. Frontiers Media S.A. 2023-02-14 /pmc/articles/PMC9971968/ /pubmed/36865366 http://dx.doi.org/10.3389/fpsyg.2023.1094150 Text en Copyright © 2023 Kummerfeld and Jones. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Psychology
Kummerfeld, Erich
Jones, Galin L.
One data set, many analysts: Implications for practicing scientists
title One data set, many analysts: Implications for practicing scientists
title_full One data set, many analysts: Implications for practicing scientists
title_fullStr One data set, many analysts: Implications for practicing scientists
title_full_unstemmed One data set, many analysts: Implications for practicing scientists
title_short One data set, many analysts: Implications for practicing scientists
title_sort one data set, many analysts: implications for practicing scientists
topic Psychology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9971968/
https://www.ncbi.nlm.nih.gov/pubmed/36865366
http://dx.doi.org/10.3389/fpsyg.2023.1094150
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