超越和度:为实现数据充足性的受访者抽样 (Q-FORS) 进行操作的定性框架
Clarissa Hsu1, Delaney Glass2, Stephanie Cruz3
1Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA; University of Washington, School of Public Health, Seattle, WA, USA.
Social science & medicine (1982)
|December 14, 2025
概括
这项研究引入了运行受访者采样 (Q-FORS) 的定性框架,以解决定性健康研究中的数据充足性问题. Q-FORS提供了一种实际的方法来确定样本大小,尊重不同的定性方法.
科学领域:
- 社会科学 社会科学 社会科学
- 卫生研究方法论 卫生研究方法论
背景情况:
- 定性研究样本大小的确定不同于定量方法.
- 和度在定性健康研究中经常被问题地应用为通用标准.
- 现有的定性方法对样本大小和数据充分性有不同的方法.
研究的目的:
- 引入用于操作化受访者抽样 (Q-FORS) 的定性框架.
- 为确定定性研究,特别是健康研究中的数据充足性提供一个实际框架.
- 提供一种透明的方法来计算定性研究中的抽样选择.
主要方法:
- 根据对定性样本大小和和文献的叙事性审查,开发了Q-FORS框架.
- 专注于数据充分性作为定性样本确定中心概念.
- 展示了Q-FORS应用程序,包括两个定性研究示例.
主要成果:
- Q-FORS通过强调数据充分性而不是一个适合所有人的和标准来澄清现有文献.
- 该框架适用于整个研究生命周期,从提案到报告.
- Q-FORS尊重定性研究的认识论基础.
结论:
- 在健康研究中,Q-FORS为定性采样提供了一种务实和认识论上合理的方法.
- 这一框架提高了质量数据收集和分析的透明度和严格性.
- 研究人员可以利用Q-FORS来证明样本大小和数据充足性的决定.
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