Triangulation in research is a method used to increase the credibility and validity of research findings by combining theories, methods, or observers in a research study. It is a procedure that enables validation of data and can be used in both quantitative and qualitative studies. Triangulation can enrich research as it offers a variety of datasets to explain differing aspects of a phenomenon of interest. It also helps refute where one dataset invalidates a supposition generated by another. It can assist in confirming a hypothesis where one set of findings confirms another set. Finally, triangulation can help explain the results of a study.

There are different types of triangulation in research, including:

• Methodological triangulation: This is the most common type of triangulation, where two or more sets of data collection using the same methodology are used, such as from qualitative data sources. Alternatively, the study may use two different data collection methods as with qualitative and quantitative.

• Investigator triangulation: This type of triangulation helps reduce the risk of observer bias and other experimenter biases.

• Theory triangulation: This means applying several different theoretical frameworks in your research instead of approaching a research question from just one theoretical perspective.

• Data source triangulation: This refers to the use of multiple methods or data sources in qualitative research to develop a comprehensive understanding of phenomena.

Triangulation is mainly used in qualitative research, but it’s also commonly applied in quantitative research. Triangulation can help ensure that fundamental biases arising from the use of a single method or a single observer are overcome, and it can help explore and explain complex human behavior using a variety of methods to offer a more balanced explanation to readers.