In mixed methods, what is triangulation?

In mixed methods, what is triangulation?

• A. Replacing data collection with trials alone.
• B. Ignoring qualitative data to save time.
• C. Using multiple data sources or methods to converge on findings.
• D. Adjusting samples for bias alone.

Answer: (C)

Triangulation in mixed methods means using more than one data source or method and looking for convergence in the findings. By combining, for example, quantitative results with qualitative insights (such as surveys and interviews), or using multiple data sources (surveys, observations, documents), you check whether different approaches point to the same conclusions. When they do converge, your conclusions gain credibility and are less likely to be the result of a single method’s biases or limitations. If they diverge, you gain valuable nuance, signaling where context or mechanisms might differ or where a deeper investigation is needed. This isn’t about replacing data collection with trials, ignoring qualitative data, or adjusting samples purely for bias. Rather, it’s about cross-checking evidence across methods or sources to strengthen or refine conclusions.

Triangulation in mixed methods means using more than one data source or method and looking for convergence in the findings. By combining, for example, quantitative results with qualitative insights (such as surveys and interviews), or using multiple data sources (surveys, observations, documents), you check whether different approaches point to the same conclusions. When they do converge, your conclusions gain credibility and are less likely to be the result of a single method’s biases or limitations. If they diverge, you gain valuable nuance, signaling where context or mechanisms might differ or where a deeper investigation is needed.

This isn’t about replacing data collection with trials, ignoring qualitative data, or adjusting samples purely for bias. Rather, it’s about cross-checking evidence across methods or sources to strengthen or refine conclusions.