Triangulation can help to increase the validity/credibility of research findings from qualitative research studies. Collecting and analyzing data from multiple sources or through multiple approaches can help us reduce any bias inherent in one particular source or approach. For example, if a main finding from one approach is completely absent or unsupported by data from another approach, the finding may have been shaped by the researcher’s bias or it may have been a random occurrence with no significance.
Following this logic, the main benefit of conducting triangulation is to reduce the likelihood of false alarm, or falsely identifying something that really doesn’t exist. This would then seem to be the exact opposite of what sample size increase can do for a statistical test, which is to reduce the likelihood of missing something that actually does exist. What do you think? Or is there a different way to characterize the function of triangulation? [250 words, 2 refeences, 2 In Text Citations. Original ScholarlyWriting Only. No ESL please]
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