Chinese General Practice ›› 2026, Vol. 29 ›› Issue (32): 4710-4715.DOI: 10.12114/j.issn.1007-9572.2026.0018

• Methodology Series in Chinese General Practice Medicine (Ⅲ) • Previous Articles     Next Articles

Interpretation of the Qualitative Framework for Operationalizing Respondent Sample Size and Case Analyses

  

  1. Department of Nursing, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
  • Received:2026-01-09 Revised:2026-04-29 Published:2026-11-15 Online:2026-10-10
  • Contact: WANG Junhui

质性研究受访者样本量操作框架解读和案例解析

  

  1. 430030 湖北省武汉市,华中科技大学同济医学院附属同济医院护理部
  • 通讯作者: 王君慧
  • 作者简介:

    作者贡献:

    付洁负责文章的构思与设计、研究资料的收集与整理、论文撰写;蒋梦瑶、蔡纯负责表格的编辑、整理;王君慧负责论文修订、文章的质量控制及审校,对文章整体负责、监督管理。

  • 基金资助:
    湖北省自然科学基金资助项目(2023AFB1118); 湖北省卫生健康科技项目(WJ2025M163)

Abstract:

In qualitative research, data saturation is widely used to judge whether a respondent sample size is sufficient. Nevertheless, there remains no widely accepted definition of what constitutes "reaching saturation". Operational guidance for implementing and reporting saturation-based decisions is also limited. To address this methodological gap, Clarissa Hsu's team proposed the Qualitative Framework for Operationalizing Respondent Sample Size(Q-FORS) in 2025. Q-FORS synthesizes 11 domains, such as the study purpose, conceptual framework, sampling and recruitment strategies, and approaches to data collection and analysis, to support a structured appraisal of data adequacy and to promote more transparent sample-size decision-making in qualitative studies. This article introduces Q-FORS and illustrates its application using concrete examples. It aims to provide health-related qualitative researchers in China with a structured yet flexible tool for reasoning about sample size, thereby improving the transparency and rigor of sample-size decision-making and reporting in qualitative research.

Key words: Qualitative research, Sample size, Saturation

摘要:

在质性研究领域,"饱和度"被广泛用于评估样本量是否充足,然而,关于如何界定"已达饱和"尚未形成共识,相关操作标准亦较为欠缺。基于这一现状,Clarissa Hsu的团队于2025年提出了质性研究受访者样本量操作框架,从研究目的、概念框架、抽样与招募策略、数据收集与分析方式等11个方面综合考量数据充分性,为质性研究样本量评估提供了结构化工具。本文对该工具进行介绍,并结合具体实例进行解读,期望为卫生健康领域的质性研究工作者提供一种兼具规范性与灵活性的样本量思考工具,促进质性研究在样本量决策与报告方面的科学性与透明度。

关键词: 质性研究, 样本量, 饱和度