datatriangulering

Datatriangulering, sometimes referred to as data triangulation, is a research methodology that involves using multiple data sources, methods, theories, or investigators to study a single phenomenon. The core idea is to cross-examine findings from different perspectives to enhance the validity and reliability of the research. By employing this approach, researchers can achieve a more comprehensive understanding of the subject matter, as discrepancies or convergences between different data points can reveal deeper insights.

There are several types of triangulation. Data triangulation involves collecting data at different times, in different places, and from different people. Methodological triangulation uses multiple research methods, such as combining qualitative interviews with quantitative surveys. Investigator triangulation involves using multiple researchers to collect and analyze data, allowing for a comparison of their interpretations. Finally, theoretical triangulation employs multiple theoretical perspectives to interpret the findings.

The primary benefit of datatriangulering is increased confidence in the research outcomes. If findings from different sources or methods converge, it strengthens the argument that the results are robust and not merely artifacts of a particular approach. Conversely, divergences can highlight areas requiring further investigation or suggest the complexity of the phenomenon under study. This technique is widely used in social sciences, education, and healthcare research.