Stephen T’s Blog Spot

The word comes from navigation. A sailor cannot fix position from a single landmark; one bearing tells you the direction to the lighthouse, not where you are. Take a bearing on a second landmark, and a third, and the lines cross at one point. That crossing is your location. No single sighting could have given it.

Social scientists borrowed the idea and the name. In 1978, Norman Denzin brought triangulation into research as a deliberate strategy: study the same thing from more than one vantage point, because any single method carries its own blind spots. He laid out four kinds. Data triangulation uses multiple sources, different people, times, and places. Investigator triangulation uses more than one researcher. Theory triangulation interprets findings through more than one framework. Methodological triangulation uses more than one method, interviews alongside records alongside a survey. The logic: if several approaches that fail in different ways all point to the same conclusion, that conclusion is hard to dismiss.

That last phrase, fail in different ways, is the whole point, and it is where triangulation is often misunderstood. The strength does not come from piling on more data. It comes from combining sources whose weaknesses are independent. A survey can be distorted by what people are willing to admit. Administrative records can be distorted by how they were entered. Interviews can be distorted by who agreed to talk. But those distortions push in different directions, so when the three still converge, the agreement means something. Stacking three sources that share the same bias just gives you the same error three times, and a false sense of confidence.

Now the part even seasoned researchers skip. We treat triangulation as a search for agreement, and feel the method has failed when the sources disagree. The methodologist Sandra Mathison argued the opposite in 1988. Convergence is only one possible outcome; sometimes the sources are inconsistent, and sometimes they flatly contradict each other, and those are not failures. They are findings. When your survey says the program is working and your interviews say people are quietly working around it, the disagreement is the most interesting thing in the room. It tells you the simple story is wrong, and points at where to look next.

For those of us in evaluation, this is daily bread. We rarely get one clean measure of whether a program worked. We get outcome data, case files, site visits, and the accounts of the people involved, each partial. Triangulating them does two jobs. When they line up, we can make a contribution claim with real confidence, because the evidence would have broken in different places if we were wrong. When they diverge, we have found the seam in the story, where the official numbers and the lived experience part ways, and that seam is usually where the real learning is.

A caution, though. Triangulation is a plan, not a label you paste on at the end. You have to choose sources that are genuinely independent, decide in advance what convergence and divergence would each mean, and resist the urge to quietly discard the source that spoils the agreement. The discarded source is often the honest one.

So the rule I keep is this. Never trust a single bearing. Convergence earns confidence; divergence earns insight; and the finding that survives three different ways of being wrong is the one worth building on.

So here is my question: When your data sources have disagreed, did you treat it as a problem to reconcile or a finding to chase, and what did the disagreement end up teaching you?