Data saturation in qualitative research means:

Data saturation in qualitative research means:

• A. Data collection continues beyond saturation for reliability
• B. Data collection continues until no new themes emerge; sample size is determined iteratively and justified without power calculations; supports credibility and dependability
• C. Stop after a fixed number of participants
• D. Stop when funding runs out

Answer: (B)

Data saturation occurs when additional data collection stops yielding new information or themes relevant to the study question. The idea is to sample and analyze in parallel, making the sample size an iterative decision: you collect data, analyze it for themes, and decide whether more participants or observations are needed based on whether new insights keep appearing. In qualitative work, power calculations aren’t used to determine sample size; instead you justify the final sample size with the richness and completeness of the data you’ve gathered. Reaching saturation supports credibility, because readers can trust that the findings reflect the range of experiences and perspectives represented in the data. It also supports dependability, since the analysis and sampling decisions are grounded in the data rather than predetermined numbers. Stopping after a fixed number of participants ignores the actual richness of the data and may miss important variation. Continuing solely because funds allow isn’t a method-based reason to collect more data, and stopping because funding runs out would undermine the study’s ability to thoroughly answer the research question.

Data saturation occurs when additional data collection stops yielding new information or themes relevant to the study question. The idea is to sample and analyze in parallel, making the sample size an iterative decision: you collect data, analyze it for themes, and decide whether more participants or observations are needed based on whether new insights keep appearing. In qualitative work, power calculations aren’t used to determine sample size; instead you justify the final sample size with the richness and completeness of the data you’ve gathered.

Reaching saturation supports credibility, because readers can trust that the findings reflect the range of experiences and perspectives represented in the data. It also supports dependability, since the analysis and sampling decisions are grounded in the data rather than predetermined numbers.

Stopping after a fixed number of participants ignores the actual richness of the data and may miss important variation. Continuing solely because funds allow isn’t a method-based reason to collect more data, and stopping because funding runs out would undermine the study’s ability to thoroughly answer the research question.