What is meant by "data triangulation" in research?

Data triangulation refers to the approach of employing multiple data sources or methods in a research study to strengthen the validity and reliability of findings. When researchers utilize this strategy, they essentially cross-verify results through different perspectives—such as combining qualitative and quantitative research methods, or using various data collection tools like surveys, interviews, and focus groups. This diversity of data sources can help mitigate biases that might arise from relying on a single approach and provides a more comprehensive understanding of the research question.

By incorporating multiple angles, researchers increase the credibility of their conclusions, making the findings more robust and trustworthy. This contrasts with analyzing data in isolation or relying on a single method, which may not capture the full picture and could lead to incomplete or misleading results. Therefore, data triangulation is a fundamental principle in research design that aims to enhance the overall quality and depth of the study.