Code Saturation Versus Meaning Saturation
Code Saturation Versus Meaning Saturation
复制标题
代码饱和度与含义饱和度
DOI：
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发表时间：
2017
影响因子：
3.2
通讯作者：
V. Marconi
中科院分区：
文献类型：
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作者：
M. Hennink;B. Kaiser;V. Marconi
饱和度是定性研究中确定样本量的一个核心指导原则，但对影响饱和度的参数的方法研究却很少。我们的研究比较了两种评估饱和度的方法：代码饱和度和意义饱和度。我们研究了在每种方法中达到饱和所需的样本量，饱和意味着什么，以及如何评估饱和。通过对25次深入访谈的研究，我们发现在9次访谈中达到了代码饱和，从而确定了主题问题的范围。然而，需要16到24次访谈才能达到意义饱和，我们对问题有了丰富的理解。因此，代码饱和度可以指示研究人员何时“听到了一切”，但是需要意义饱和度来“理解一切”。我们使用我们的研究结果来开发影响饱和度的参数，这些参数可用于估计定性研究提案的样本量，或在出版物中记录实现饱和度的理由。
Saturation is a core guiding principle to determine sample sizes in qualitative research, yet little methodological research exists on parameters that influence saturation. Our study compared two approaches to assessing saturation: code saturation and meaning saturation. We examined sample sizes needed to reach saturation in each approach, what saturation meant, and how to assess saturation. Examining 25 in-depth interviews, we found that code saturation was reached at nine interviews, whereby the range of thematic issues was identified. However, 16 to 24 interviews were needed to reach meaning saturation where we developed a richly textured understanding of issues. Thus, code saturation may indicate when researchers have “heard it all,” but meaning saturation is needed to “understand it all.” We used our results to develop parameters that influence saturation, which may be used to estimate sample sizes for qualitative research proposals or to document in publications the grounds on which saturation was achieved.