Triangulation is a technique used in data analysis to increase the reliability and validity of results. Triangulation involves using multiple sources of data and methods of analysis to address a research question or issue. The goal of triangulation is to cross-check and corroborate findings, and to provide a more comprehensive and robust understanding of a research question.
The big questions are ones that position your analysis in the context of your problem or decision:
- What does the data mean in context of the business / decision / problem?
- How does it compare to what’s happened before?
- How does the data compare with comparison data or with the competition?
There are multiple approaches to triangulation, including:
- Choose multiple sources of data: Collecting data from multiple sources such as surveys, interviews, observation, or existing databases, provides a more complete and robust picture of the research question or issue.
- Use multiple methods of analysis: Using different methods of analysis, such as qualitative analysis, quantitative analysis, or mixed methods analysis, provides different perspectives on the data, increasing the reliability and validity of the results.
- Compare findings from different sources and methods: By comparing the findings from different sources and methods, the researcher can identify any similarities or differences and make inferences about the robustness of the results.
- Integration of findings: Finally, the researcher should integrate the findings from different sources and methods to form a more comprehensive understanding of the research question or issue. This integration should take into account any similarities or differences in the findings and consider any potential limitations of the data or methods used.
Triangulation can be particularly useful in complex research questions or issues where data is limited or unreliable. By using multiple sources of data and methods of analysis, triangulation can help increase the reliability and validity of results, and provide a more robust understanding of the research question or issue.