Combining qualitative and quantitative research within mixed method research designs: A methodological review

Abstract

Objectives It has been argued that mixed methods research can be useful in nursing and health science because of the complexity of the phenomena studied. However, the integration of qualitative and quantitative approaches continues to be one of much debate and there is a need for a rigorous framework for designing and interpreting mixed methods research. This paper explores the analytical approaches (i.e. parallel, concurrent or sequential) used in mixed methods studies within healthcare and exemplifies the use of triangulation as a methodological metaphor for drawing inferences from qualitative and quantitative findings originating from such analyses. Design This review of the literature used systematic principles in searching CINAHL, Medline and PsycINFO for healthcare research studies which employed a mixed methods approach and were published in the English language between January 1999 and September 2009. Results In total, 168 studies were included in the results. Most studies originated in the United States of America (USA), the United Kingdom (UK) and Canada. The analytic approach most widely used was parallel data analysis. A number of studies used sequential data analysis; far fewer studies employed concurrent data analysis. Very few of these studies clearly articulated the purpose for using a mixed methods design. The use of the methodological metaphor of triangulation on convergent, complementary, and divergent results from mixed methods studies is exemplified and an example of developing theory from such data is provided. Conclusion A trend for conducting parallel data analysis on quantitative and qualitative data in mixed methods healthcare research has been identified in the studies included in this review. Using triangulation as a methodological metaphor can facilitate the integration of qualitative and quantitative findings, help researchers to clarify their theoretical propositions and the basis of their results. This can offer a better understanding of the links between theory and empirical findings, challenge theoretical assumptions and develop new theory.