Which statement best describes data saturation in qualitative research?

• A. It occurs when data collection must continue to meet a predetermined sample size.
• B. It occurs when no new themes emerge from data collection.
• C. It means statistical power has been reached.
• D. It is unrelated to the breadth of themes.

Answer: (B)

In qualitative research, data saturation is reached when gathering more data no longer yields new insights, themes, or patterns. This means you’ve explored the topic enough that the range of perspectives has been adequately captured, and additional data would tend to repeat what you’ve already found.

The best answer reflects this idea because it emphasizes the stopping point is about redundancy of information, not hitting a pre-set number. In practice, researchers analyze data as they collect it, coding and comparing new material with what’s already found. When subsequent interviews or sources keep yielding familiar ideas and no new themes emerge, saturation is achieved. That signals the data are rich enough to support the conclusions.

Why the other notions don’t fit: continuing to collect data to meet a predetermined sample size misses the core point—that the value lies in the breadth and depth of themes, not a fixed count. Statistical power belongs to quantitative research, focusing on probability and detectability of effects, not the qualitative aim of capturing themes. And saturation is inherently tied to breadth—it’s about having a comprehensive set of themes represented in the data, not being unrelated to how wide the topics appear.