Triangulation

Triangulation facilitates validation of data through cross verification from more than two sources.

It tests the consistency of findings obtained through different instruments and increases the chance to control, or at least assess some of the threats or multiple causes influencing our results.

Triangulation is not just about validation but about deepening and widening one’s understanding. It can be used to produce innovation in conceptual framing. It can lead to multi-perspective meta-interpretations. [Triangulation is an] attempt to map out, or explain more fully, the richness and complexity of human behavior by studying it from more than one standpoint? - Cohen and Manion

Types

Denzin (1973, p.301) proposes four basic types of triangulation:

• Data triangulation: involves time, space, and persons
• Investigator triangulation: involves multiple researchers in an investigation
• Theory triangulation: involves using more than one theoretical scheme in the interpretation of the phenomenon
• Methodological triangulation: involves using more than one option to gather data, such as interviews, observations, questionnaires, and documents.

Reasons for triangulation

Carvalho and White (1997) propose four reasons for undertaking triangulation:

• Enriching: The outputs of different informal and formal instruments add value to each other by explaining different aspects of an issue
• Refuting: Where one set of options disproves a hypothesis generated by another set of options.
• Confirming: Where one set of options confirms a hypothesis generated by another set of options
• Explaining: Where one set of options sheds light on unexpected findings derived from another set of options.

Triangulation to minimize bias

The problem with relying on just one option is to do with bias. There are several types of bias encountered in research, and triangulation can help with most of them.

• Measurement bias – Measurement bias is caused by the way in which you collect data. Triangulation allows you to combine individual and group research options to help reduce bias such as peer pressure on focus group participants.
• Sampling bias – Sampling bias is when you don’t cover all of the population you’re studying (omission bias) or you cover only some parts because it’s more convenient (inclusion bias). Triangulation combines the different strengths of these options to ensure you getting sufficient coverage.
• Procedural bias – Procedural bias occurs when participants are put under some kind of pressure to provide information. For example, doing “vox pop” style interrupt polls might catch the participants unaware and thus affect their answers. Triangulation allows us to combine short engagements with longer engagements where participants have more time to give considered responses.

Using an evaluation matrix to check triangulation

An evaluation matrix, as shown below, will help you check that the planned data collection will cover all the KEQs, see if there is sufficient triangulation between different data sources, and help you design questionnaires, interview schedules, data extraction tools for project records, and observation tools, to ensure they gather the necessary data.

Carvalho, S. and White, H. (1997). Combining the quantitative and qualitative approaches to poverty measurement and analysis: The practice and the potential. World Bank Technical Paper 366. Washington, D.C.: World Bank

Cohen, L. & Manion, L. Research methods in education. Routledge.

Denzin, Norman K. (1973). The research act: A theoretical introduction to sociological methods. New Jersey: Transaction Publishers.

Kennedy, Patrick. (2009). How to combine multiple research options: Practical Triangulation. http://johnnyholland.org/2009/08/20/practical-triangulation (archived link)

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