In the context of AFIT, what does "data triangulation" refer to?

Data triangulation refers to the practice of using multiple data sources or methods to enhance the validity of research findings. By incorporating diverse perspectives or different methodologies, researchers can cross-verify their results, leading to a more robust and credible conclusion. This approach reduces the likelihood of bias that might arise from relying solely on a single source or method, ultimately strengthening the research's reliability.

For instance, in an educational context, if a study on student performance uses test scores, surveys, and observational methods, the convergence of findings from these varied sources can provide a more comprehensive understanding of the factors influencing student success. This multi-faceted approach is particularly valuable in research settings where the complexity of the topic necessitates a broader examination.

Other options do not contribute to the validity of research findings in the same way. Relying on a single data source may lead to skewed results, while collecting data from random samples or applying one research method across different studies could limit the depth and comprehensiveness that triangulation provides.