Perhaps the largest advantage of interpreting mixed methods data is comprehensiveness (Mason, 2006). In other words, examining a research problem through the collecting, analysis, interpretation, and ultimately, the connection or “mixing” data from different aspects or considerations; provides a more complete answer or understanding of the search problem than could be accomplished by only using one set of data. A second of interpreting mixed methods data, is that it will force the research to develop a better understanding of the research in the initial stages of the process. Without the ability to accomplish this, the researcher will not be able to know which aspects of the research will be able to provide different but relevant data sets that can be appropriately mixed to accomplish the comprehensiveness discussed above.
Conversely, an important disadvantage of interpreting mixed methods data is that lack of comprehensiveness in the actual data that is collected. Research is normally a finite endeavor that is substantially influenced by time, resources and funds. Accordingly, if research seeks to obtain in a mixed methods fashion, the time, resources, and funds that are provided for the research will have to be split among the various methods. Naturally, this can lead to compromises in the size of the data sample, the time limit of the study, and the breadth of the research. In other words, mixed methods data, while rich and varied may not provide enough to provide a complete understanding of the research problem. Another issue with mixed methods is bias (Symonds & Gorard, n.d). Here bias refers to the tendency of a researcher to favor one form of research over another, such as qualitative over quantitative or mixed methods over more traditional methods (Symonds & Gorard, n.d). These biases can work to false promote one form of research over another without a thorough examination as to which is indeed better, or whether there is no “better” form but rather different form that work better than others in a particular situation.
References
Mason, J. (2006, Apr.). Six strategies for mixing methods and linking data in social science research. Retrieved from http://eprints.ncrm.ac.uk/482/1/0406_six%2520strategies%2520for%2520mixing%2520methods.pdf
Symonds, J.E. & Gorard, S. (n.d.). The death of mixed methods: Research labels and their casualties. Retrieved from http://www.leeds.ac.uk/educol/documents/174130.pdf
