What does data triangulation refer to in research?

Data triangulation refers to the application of multiple data sources or methods to study a phenomenon. This approach enhances the validity and credibility of research findings because it allows for the convergence of information from different sources, offering a more comprehensive understanding of the topic being investigated. By employing various methods—such as combining qualitative and quantitative data, or using multiple data sources like surveys, interviews, and observations—researchers can cross-verify results, leading to richer insights and reducing the potential for bias that might arise from relying on a single source of data. Triangulation is particularly useful in complex fields such as psychology, where human behavior can be influenced by numerous factors.

The other options do not accurately capture the essence of data triangulation. For instance, relying on one data source goes against the fundamental principle of triangulation, which is to gather diverse perspectives. The concept of standardizing data collection methods pertains to ensuring consistency in how data is gathered, which does not involve the idea of using multiple sources or methods. Lastly, focusing solely on qualitative sources limits the breadth of data gathering and does not exemplify the idea of triangulation, which seeks to integrate diverse types of information.