質性研究訪談個案數之判定：資料飽和概念之使用

Data Saturation and the Determination of Sample size in Qualitative Interview-Based Research

近年來質性研究方法學在醫學、護理以及健康照護相關研究領域的運用越來越普遍，其研究發現對於醫療照護品質的現況提供多面向的檢視與貢獻。長期以來，相較於量性研究，質性研究的樣本數傾向少數的特性已是既定印象，然而樣本數之判定一直難有定論，質性研究者普遍將資料飽和的概念視為決定樣本數的黃金定律，但對於概念的定義以及其具體操作方式卻鮮少有文章清楚說明。鑑於此，本文將回顧近年來與質性研究資料飽和相關的重要文獻，期能釐清資料飽和的定義、概念、影響因素與操作方式，藉此提供質性研究者對於資料飽和概念的理解，在設計質性研究時，判定合理的個案數。

In recent years, the use of qualitative research approaches in the fields of medicine, nursing, and healthcare has become increasingly common, and the findings of such studies have provided a multi-faceted review of the quality of medical care. It has long been established that sample sizes in qualitative research tend to be smaller than those used in quantitative research; however, the method for determining the appropriate sample size remains ambiguous. Qualitative researchers generally regard data saturation as the gold standard for determining the appropriate ample size, but few articles have clearly illustrated the concept of data saturation and its specific operating procedures. In view of this, the present paper reviews key studies on the use of data saturation in qualitative research-with the aim of clarifying the definition, concept, influencing factors, and operational methods of data saturation. Our goal is to provide qualitative researchers with an understanding of data saturation to help them determine a reasonable sample size when designing a qualitative study.