What does "data triangulation" refer to in research?

Data triangulation refers to the validation of research findings using multiple data sources or methods. This approach enhances the credibility and reliability of the research results by allowing the researcher to cross-verify information. By integrating various data sources, such as combining surveys, interviews, and existing datasets, researchers can obtain a more comprehensive understanding of the research question and reduce the likelihood of bias or error that might arise from relying on a single source.

This method is particularly valuable because it allows for the identification of patterns and inconsistencies across different data sources, leading to more robust conclusions. Triangulation can also provide deeper insights by revealing aspects of the research topic that may not be captured through a limited methodology.

The other options reflect different approaches or concepts not aligned with the essence of data triangulation. For instance, relying on a singular data source lacks the breadth and reliability that triangulation offers. The combination of qualitative and quantitative approaches is a valid research strategy, but it does not specifically address the broader concept of using multiple data sources. Lastly, the sequential use of different methodologies pertains to methodological pluralism but does not encapsulate the core idea of data triangulation, which emphasizes contemporaneous use of varied data sources for validation.