What is triangulation in data collection and why is it valuable?

• A. Using multiple data sources or methods to confirm findings, increasing validity.
• B. Ignoring outliers.
• C. Reducing data sources to save time.
• D. Using the same data source repeatedly to improve precision.

Answer: (A)

Triangulation means gathering information from more than one data source or using multiple methods to check results. By looking at the same question from different angles—different people, different tools, or different types of data—you cross-check what you find. This helps confirm that the outcome isn’t just a quirk of one method or dataset and boosts the trustworthiness of the conclusions. For example, studying urban growth with census data, satellite images, and field observations, and then seeing the same trend across all three, makes the finding more credible because it’s supported from multiple perspectives.

That’s why this option is the best choice: it captures the idea of confirming findings by using diverse sources or methods to improve validity. The other ideas don’t fit triangulation: ignoring outliers isn’t about cross-checking with multiple sources; reducing data sources misses the purpose of comparing across different datasets; and using the same data source repeatedly doesn’t provide the cross-verification that triangulation aims for.