(I Can't Get No) Saturation: A simulation and guidelines for sample sizes in qualitative research

Area of Science:

• Qualitative Research Methodology
• Data Saturation in Research
• Sampling Strategies

Background:

• Qualitative research relies on achieving theoretical saturation, where no new codes emerge.
• Understanding the optimal sample size is crucial for efficient and rigorous qualitative studies.

Purpose of the Study:

• To determine the minimum sample size needed to reach theoretical saturation in qualitative research.
• To compare the efficiency of different sampling scenarios for achieving saturation.

Main Methods:

• Conceptualized populations with sub-populations containing information sources and codes.
• Simulated three sampling scenarios: random chance, minimal information, and maximum information.
• Assessed minimum sample size requirements for each scenario across varying hypothetical populations.

Main Results:

• Theoretical saturation is more influenced by the probability of observing codes than the total number of codes.
• Minimal and maximal information sampling scenarios are more efficient than random chance sampling.
• Efficient sampling scenarios result in fewer code repetitions for validation.

Conclusions:

• Researchers should consider purposive sampling strategies to achieve theoretical saturation efficiently.
• The 'minimum information' sampling scenario is recommended for practical qualitative research.
• Guidelines for purposive sampling are provided to enhance the rigor of qualitative studies.