Optimal Sample Size in Qualitative Research: Balancing Data Richness, Credibility, and Transferability

Determining an optimal sample size remains one of the most contentious methodological issues in qualitative research. While numerical recommendations are frequently cited, they often overlook the epistemological, methodological, and analytical considerations that underpin qualitative inquiry. This paper critically reviews contemporary perspectives on qualitative sample size determination with the aim of developing an integrated framework that balances data richness, credibility, and transferability. Drawing on an extensive narrative review of methodological literature retrieved from major academic databases, the study synthesises evidence on data saturation, theoretical saturation, information power, conceptual depth, meaning saturation, and pragmatic sampling considerations across major qualitative traditions, including phenomenology, grounded theory, ethnography, case study, narrative inquiry, interpretative phenomenological analysis, and qualitative interviews. The review demonstrates that no universally optimal numerical sample size exists; instead, sample adequacy depends on research objectives, epistemological assumptions, methodological design, sample specificity, analytical strategy, and the quality and richness of the data. The findings further indicate that transparent methodological justification is more important than compliance with arbitrary numerical thresholds. Building on these insights, the paper proposes principles for defensible sample size justification that encourage researchers to align sampling decisions with research purpose, methodological logic, and ongoing analytical development. By integrating previously fragmented theoretical perspectives into a coherent conceptual framework, this study advances methodological clarity and contributes practical guidance for researchers, supervisors, journal reviewers, and research ethics committees seeking to enhance the rigour, transparency, and credibility of qualitative research.

Sample size; Data saturation; Information power; Theoretical saturation; Conceptual depth

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