Combining quantitative and qualitative methods: Triangulation

Some researchers experienced in using a combination of qualitative and quantitative methods have reported effects and results that transcend mere complementarily. Blending and integrating methods and data in studying the same phenomena can "...capture a more complete, holistic and contextual portrayal" of the subject under study, by eliciting data leading to new hypotheses or conclusions, for which single methods would be blind.

In most research designs using triangulation methods, Jick and other authors have pointed out the hidden assumption of triangulation: that the weaknesses and limitations of each individual method will be counterbalanced by the other method, exploiting the assets, and neutralizing, rather that compounding, the liabilities [15].

The use of multiple methods in research has been applied for over a decade. Its introduction into the health field has been relatively recent, and yet there seems to be reluctance to accept it. This is partly because the dominant paradigm of the natural sciences in biomedical research. has looked with disdain at the use of qualitative methods proposed by social scientists. On the other hand, norms and requirements of scientific publications have placed rigid criteria for review and acceptance of manuscripts, introducing biases in the selection of studies for publication. Refusal is often based on quantitative criteria: "lack of replicability," "small samples" invalidating generalisation of results, or "no statistically significant differences."

Furthermore, most existing research training manuals emphasise the use of single methods, either quantitative or qualitative, and there is a lack of instructional material that will guide students and researchers in the actual collection, analysis and interpretation of data from different perspectives using the "triangulation" approach. The analysis of qualitative-quantitative data requires experience and skills in the processing and interpretation of both "hard" and "soft" data.