Let's talk about triangulation... (3 common myths about triangulation in research)

Triangulation in qualitative research is often misunderstood as a numbers game—especially as a requirement to use exactly three methods. In practice, triangulation simply means using more than one source or approach to strengthen the credibility of what emerges from a study. That matters because students face pressure to “tick the triangulation box,” even though many successful qualitative investigations rely on a single method. The key takeaway is that triangulation is about the quality and fit of evidence, not the quantity of techniques.

A second common myth treats triangulation as a truth-checking mechanism aimed at validating whether each participant is “telling the truth.” While it’s possible to design a study that compares what an individual says in an interview with what appears in other materials (such as reflective journals or focus group contributions from the same person), that isn’t the dominant purpose of triangulation. More commonly cited is triangulation as a way to build a comprehensive, in-depth understanding of the phenomenon under study. In this framing, multiple data sources contribute to stronger claims and greater validity—not by verifying individual statements in isolation, but by showing how different evidence converges to illuminate the research question.

To clarify how triangulation functions, the transcript points to four reasons attributed to Carvalo and White: enriching, refuting, confirming, and explaining. “Enriching” is presented as the most common rationale—different formal and informal instruments add value by illuminating different aspects of an issue. “Refuting” and “confirming” involve testing competing ideas: one set of evidence disproves or supports a hypothesis generated by another. “Explaining” addresses unexpected findings, using additional evidence to shed light on why something emerged—an approach that resembles the logic of mixed methods, where one phase (often quantitative) is followed by another (often qualitative) to interpret results.

The third myth narrows triangulation to data collection methods only, but the transcript argues that triangulation also includes other “types,” depending on the classification used. Examples include theoretical triangulation (drawing on different theoretical perspectives or frameworks), investigator triangulation (using multiple investigators during analysis, such as through intercoder reliability), and data triangulation (using data from different people collected at different times). Methodological triangulation is also mentioned, though the boundaries can blur—some classifications treat it as differences in data collection methods, while others include differences in research designs (for instance, quantitative versus qualitative), which overlaps with mixed methods. The practical guidance is straightforward: choose a classification, follow it consistently, and make the terminology transparent.

Overall, triangulation is portrayed as a flexible strategy for strengthening understanding and validity through multiple, well-justified lines of evidence—not as a rigid formula of three methods or a simple participant-by-participant verification exercise.