What does “data triangulation” involve?

Data triangulation involves using multiple data sources to enhance the credibility and validity of research findings. This approach allows researchers to cross-verify information, leading to more robust conclusions. By incorporating various types of data from different origins, researchers can mitigate biases that may arise from relying on a single source or method. For instance, combining quantitative data from surveys with qualitative data from interviews can provide a more comprehensive understanding of the research question.

The focus on utilizing diverse data sources helps to paint a fuller picture of the issue being studied, ultimately improving the reliability of the research outcomes. A single method or source may overlook important perspectives or nuances, thereby weakening the overall findings. Triangulation serves as a powerful strategy to strengthen the research methodology and ensure that conclusions are well-supported by the evidence collected.