What does it mean to triangulate data?

Data triangulation is the use of a variety of data sources, including time, space and persons, in a study. Findings can be corroborated and any weaknesses in the data can be compensated for by the strengths of other data, thereby increasing the validity and reli- ability of the results.

What is triangulation data collection?

Triangulation is a technique to analyze results of the same study using different methods of data collection. It is used for three main purposes: to enhance validity, to create a more in-depth picture of a research problem, and to interrogate different ways of understanding a research problem.

What is an example of data triangulation?

First, data triangulation involves using multiple sources of data in an investigation. In a research study examining hospital staff morale, for example, interviews with medical personnel might be compared and cross-checked with staff surveys and records of focus groups consisting of hospital employees.

How do you triangulate data sources?

Data triangulation involves the use of different sources of data/information. A key strategy is to categorize each group or type of stakeholder for the program that you are evaluating. Then, be certain to include a comparable number of people from each stakeholder group in the evaluation study.

Why is triangulation of data important?

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.

Why is data triangulation important?

What are the advantages of triangulation in research?

Triangulation can help: Reduce bias that comes from using a single method, theory, or investigator. Enhance validity by approaching the same topic with different tools. Establish credibility by giving you a complete picture of the research problem.