利用计算机辅助定性数据分析软件在归纳研究中建立透明度和可信度

IF 8.9 2区 管理学 Q1 MANAGEMENT Organizational Research Methods Pub Date : 2021-01-01 DOI:10.1177/1094428119865016
P. O’Kane, Anne D. Smith, Michael P. Lerman
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引用次数: 58

摘要

许多学者呼吁进行定性研究,以证明数据分析过程的透明度和可信度。然而,这些过程,特别是在归纳研究中,往往仍然笼罩在神秘之中。我们建议,计算机辅助/辅助定性数据分析软件(CAQDAS)可以支持定性研究人员以透明的方式展示他们的分析和发现,从而提高可信度。为此,我们提出、描述并举例说明了六个CAQDAS构建块、三个组合CAQDAS技术和两个编码器一致性检查的工作示例。我们认为,这些技术为研究人员提供了以透明的方式撰写方法和发现的语言,并且它们的适当使用提高了研究项目的可信度。在一系列归纳研究过程中,很少讨论具体的CAQDAS技术。因此,我们将本文视为关于CAQDAS支持归纳定性研究的效用的对话的开始。
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Building Transparency and Trustworthiness in Inductive Research Through Computer-Aided Qualitative Data Analysis Software
Many scholars have called for qualitative research to demonstrate transparency and trustworthiness in the data analysis process. Yet these processes, particularly within inductive research, often remain shrouded in mystery. We suggest that computer-aided/assisted qualitative data analysis software (CAQDAS) can support qualitative researchers in their efforts to present their analysis and findings in a transparent way, thus enhancing trustworthiness. To this end, we propose, describe, and illustrate working examples of six CAQDAS building blocks, three combined CAQDAS techniques, and two coder consistency checks. We argue that these techniques give researchers the language to write about their methods and findings in a transparent manner and that their appropriate use enhances a research project’s trustworthiness. Specific CAQDAS techniques are rarely discussed across an array of inductive research processes. Thus, we see this article as the beginning of a conversation about the utility of CAQDAS to support inductive qualitative research.
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来源期刊
CiteScore
23.20
自引率
3.20%
发文量
17
期刊介绍: Organizational Research Methods (ORM) was founded with the aim of introducing pertinent methodological advancements to researchers in organizational sciences. The objective of ORM is to promote the application of current and emerging methodologies to advance both theory and research practices. Articles are expected to be comprehensible to readers with a background consistent with the methodological and statistical training provided in contemporary organizational sciences doctoral programs. The text should be presented in a manner that facilitates accessibility. For instance, highly technical content should be placed in appendices, and authors are encouraged to include example data and computer code when relevant. Additionally, authors should explicitly outline how their contribution has the potential to advance organizational theory and research practice.
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