Beyond saturation: A qualitative framework for operationalizing respondent sampling (Q-FORS) for data adequacy.

Area of Science:

• Social Sciences
• Health Research Methodology

Background:

• Qualitative research sample size determination differs from quantitative methods.
• Saturation is often problematically applied as a universal standard in qualitative health research.
• Existing qualitative methods have varied approaches to sample size and data adequacy.

Purpose of the Study:

• To introduce the Qualitative Framework for Operationalizing Respondent Sampling (Q-FORS).
• To provide a practical framework for determining data adequacy in qualitative research, particularly in health.
• To offer a transparent method for accounting for sampling choices in qualitative studies.

Main Methods:

• Developed the Q-FORS framework based on a narrative review of qualitative sample size and saturation literature.
• Focused on data adequacy as the central concept for qualitative sample determination.
• Demonstrated Q-FORS application with two qualitative research examples.

Main Results:

• Q-FORS clarifies existing literature by emphasizing data adequacy over a one-size-fits-all saturation standard.
• The framework is applicable across the research lifecycle, from proposal to reporting.
• Q-FORS respects the epistemological underpinnings of qualitative research.

Conclusions:

• Q-FORS provides a pragmatic and epistemologically sound approach to qualitative sampling in health research.
• This framework enhances transparency and rigor in qualitative data collection and analysis.
• Researchers can utilize Q-FORS to justify sample size and data adequacy decisions.