现在是分享定性研究数据的时候了吗?

IF 11.7 Q1 PSYCHOLOGY, MULTIDISCIPLINARY Qualitative psychology (Washington, D.C.) Pub Date : 2018-11-01 Epub Date: 2017-03-16 DOI:10.1037/qup0000076
James M DuBois, Michelle Strait, Heidi Walsh
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引用次数: 0

摘要

美国国立卫生研究院和美国国家科学基金会的政策,以及围绕心理学重要研究成果无法重现的丑闻,使得共享研究数据的呼声日益高涨。这些讨论大多集中在定量而非定性研究数据上。本文探讨了围绕共享定性研究数据的科学、伦理和政策问题。我们考虑了共享数据的优势,包括可以验证研究结果、以经济的方式促进新的研究、支持研究教育以及提高公众对科学的信任。然后,我们研究了数据归档和共享的标准程序,如数据匿名化和建立数据使用协议。最后,我们探讨了定性研究数据共享的一系列问题,如在解释数据时关系的重要性、重新识别参与者身份的风险、与同意和数据所有权相关的问题,以及数据文档和存放给研究人员带来的负担。针对每一个问题,我们都确定了能够实现数据共享的方案,或描述了在哪些条件下可能会从数据存储库中扣留部分数据。最后,我们建议将共享定性数据作为默认假设,除非存在无法通过匿名化数据等标准数据存放实践或数据使用协议解决的问题。
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Is It Time to Share Qualitative Research Data?

Policies by the National Institutes of Health and the National Science Foundation, as well as scandals surrounding failures to reproduce the findings of key studies in psychology, have generated increased calls for sharing research data. Most of these discussions have focused on quantitative, rather than qualitative, research data. This paper examines scientific, ethical, and policy issues surrounding sharing qualitative research data. We consider advantages of sharing data, including enabling verification of findings, promoting new research in an economical manner, supporting research education, and fostering public trust in science. We then examine standard procedures for archiving and sharing data, such as anonymizing data and establishing data use agreements. Finally, we engage a series of concerns with sharing qualitative research data such as the importance of relationships in interpreting data, the risk of re-identifying participants, issues surrounding consent and data ownership, and the burden of data documentation and depositing on researchers. For each concern, we identify options that enable data sharing or describe conditions under which select data might be withheld from a data repository. We conclude by suggesting that the default assumption should be that qualitative data will be shared unless concerns exist that cannot be addressed through standard data depositing practices such as anonymizing data or through data use agreements.

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