Sample size for saturation in qualitative research: Debates, definitions, and strategies

Abstract

Data saturation is a cornerstone concept in qualitative research, ensuring that data collection ceases once no new themes, insights, or patterns emerge. This concept is critical for achieving methodological rigor, as saturation enhances the credibility and completeness of research findings. Despite its central role, debates persist regarding the point at which saturation is achieved, especially as it varies across qualitative methodologies such as grounded theory, phenomenology, and ethnography. Contemporary scholars argue for a flexible approach to sample sizes and saturation criteria, balancing comprehensive data gathering with respect for emerging themes and contextual sensitivity. This article explores the theoretical foundations, practical applications, and controversies surrounding data saturation. Additionally, it offers recommendations for researchers on determining sample sizes and 16-ietms checklist for achieving saturation, aiming to improve research quality while addressing the methodological challenges inherent in qualitative research.