What is triangulation in social research?

Triangulation in social research is a technique to increase credibility of research outcomes. Borrowed from navigational and land surveying techniques, it aims to overcome biases in single method, researchers, data sets and/or theories.

There are 4 types of triangulation:

  1. Methodological – two or more methods are used to collect data
  2. Data – two or more different data sets
  3. Researcher – two or more researchers investigate
  4. Theoretical – two or more theories approach the often described as a way of ensuring some form of ‘truth’ in research results. This is one reason to use triangulation, but another is to intentionally draw out some of the differences

As an anthropologist I think we constantly aim to reduce the influence of bias in research we do. A competent, self-aware anthropologist is very good at observing phenomena from multiple viewpoints. Of course we can’t always overcome our own biases,  so designing your research (and synthesis) with triangulation in mind can contribute to overall outcomes.

Courses in ethnographic and design research

EPIC have announced great new learning initiatives for its members. A series of courses and talks will be offered throughout the year in order to deliver on EPIC’s purpose to advance ethnography in industry. Some of the courses look to be taken from popular seminars from the 2016 conference. The only downside is the timezone issue if you happen to be based in incompatible timezones with the US, like Oz…

You can find out what courses and talks are offered here

How to uncover the difference between what users say and what they will do

I had a beautiful moment today in customer research of revealing the difference between what people say they do and what they will actually do. I had created an activity for participants to complete in pairs around how they wanted an app to work which involved them choosing desired features.

As I looked around the room and saw the feature wishlists of the pairs forming on the walls around the room, I could see a range of features we had discounted based on earlier user research. I was trying to figure out what we had possibly missed from our earlier analysis, or what may be unique to these particular users we had recruited. We finished the activity and the pairs shared their rationale behind each of the features they had on their lists. Each pair explained their thinking clearly and quite convincingly. While I was listening to them, it made sense why they wanted these features.

However, once I asked them to choose their top three features, the conversations were so rich and full of data about what really mattered that those logical arguments from the activity before started to fall away. Going through a process of prioritising and explaining why they were choosing what they wanted the app to be able to do gave us far more insight into what this app needed to be able to provide. We started to move past rational thinking and tap into how the app could fit into their lives and habits. This activity was really helping to get to the things that really mattered to potential users, and therefore what we needed to focus on. It was a great reminder of how ranking and prioritisation can help to tap into what users really want, and move beyond what they think will be ‘nice-to -haves’, even if they can make a logical reason for wanting a feature.