Data triangulation is a research method used to enhance the credibility and validity of research findings by cross-verifying information from multiple sources or using multiple methods. It involves the use of various data sources, data collection techniques, or theories to gather information or interpret research results. Data triangulation can be applied in a wide range of fields, including social sciences, natural sciences, and market research. There are several common forms of data triangulation:
- Methodological Triangulation: This involves using multiple research methods or data collection techniques to investigate the same phenomenon. For example, a researcher studying the impact of a social program might use both surveys and interviews to gather data.
- Data Source Triangulation: In this approach, researchers collect data from different sources or perspectives. For instance, in a study on consumer behavior, data might be collected from surveys, interviews, and sales records to ensure a more comprehensive view.
- Investigator Triangulation: This form of triangulation involves multiple researchers or investigators independently collecting and analyzing data. Their individual findings can be compared and combined to reduce bias and improve reliability.
- Theoretical Triangulation: Researchers may use different theoretical frameworks or perspectives to analyze the same data. This approach can help explore multiple interpretations and provide a more comprehensive understanding of the phenomenon.
