What is meant by "data triangulation" in research?

Data triangulation refers to the practice of using multiple methods of data collection in a research study to enhance the validity and reliability of the findings. By employing different methods—such as surveys, interviews, and observations—researchers can cross-verify results and ensure that the conclusions drawn are robust and well-supported. This approach helps mitigate the biases or limitations inherent in any single method and provides a more comprehensive understanding of the research question at hand.

In contrast, using a single method for data collection or focusing on one data source can lead to incomplete or skewed results, as it may not capture the full range of variability and insights available in the data. Combining findings from various studies, while valuable for broader theoretical implications, does not necessarily constitute data triangulation, which specifically emphasizes diverse methods within the same study. Thus, applying multiple methods enriches the research process, leading to more reliable and valid outcomes.