Triangles and their Importance

Before reading the articles and lecture, I had very little knowledge of triangulation. Having gone through the Research course last fall, I knew how and why it was done but nothing more. Triangulation is very useful as it takes more than one source/study into account and brings them all together to form a much larger data set and provides a lot more information. Using triangulation helps with the limitations aspect and reduces biases that a researcher may face.

Guion discusses the validity aspect when dealing with triangulation. Qualitative researchers especially use triangulation to check how valid their studies actually are. Data triangulation seems like the way to go when dealing with program evaluations. Not only is it one of the more popular types but it is easy to use. With data triangulation, you can categorize the groups of stakeholders involved with the program. The triangulation comes into play when finding similarities between the different groups' responses. Guion says that "if every stakeholder...sees an outcome then it is more than likely to be a true outcome".

Another type of triangulation is the investigator triangulation. When you are dealing with program evaluations and using investigator triangulation, you are going to be using many different evaluators but they will all use the same method. Eventually the evaluators will compare their findings. This can be more time consuming with assembling the team and needing everyone to go through the process when time allows it.

I guess preference really depends on how much time you have, how many people are helping, as well as what is being evaluated and studied. For a program evaluation, you are going to want to enough opinions and views of the program to form a proper evaluation.