Mixed Methods and Considering the Case

So, I hate to say it, but I was kind of bored reading the mixed methods chapter. I’m sure it is because it is not relevant to my thesis research, and not because the information itself was actually boring. Anyway, it seems a lot of mixed methods research has to do with explaining mixed methods—what it is, why you are using, how you use it, who has used it before, which strategy you are using, the order you use qualitative and quantitative methods/data, the type of data (along with a visual), etc. Apparently, this is due to mixed methods research being “relatively new in the social and human sciences as a distinct research approach” (204). I’m sure there is just as much justification in the other methods of research, but for some reason this stood out so much more in this chapter.

There were two parts that I found the most interesting in this chapter, the strategies and data analysis. Honestly, I didn’t think that there would be so many different strategies in a mixed methods design. The six of the twelve strategies outlined in the chapter are sequential explanatory, sequential exploratory, sequential transformative, concurrent triangulation, concurrent embedded, and concurrent transformative. In all three sequential strategies, the data collection is two-phase with one following the other. In sequential explanatory, the quantitative data is collected and analyzed first, and secondly, the qualitative data is collected and analyzed in order to “explain and interpret [the] quantitative results” (211). Sequential exploratory is the same approach, but the order is switched—qualitative data is collected and analyzed first, and then quantitative data is collected and analyzed in order to “assist in the interpretation of qualitative findings” (211). Side note: I found it interesting that this model would make a qualitative study “more palatable” for an audience unfamiliar with qualitative research (212). The sequential transformative uses a theoretical lens to “guide the study” (212), and it doesn’t matter if qualitative or quantitative comes first or is used to support the other.

 The same is with the three concurrent strategies in which both qualitative and quantitative data are collected simultaneously. In concurrent triangulation, both qualitative and quantitative data are collected at the same time, and then the results merged or integrated/compared into two databases for a side by side discussion (213). Concurrent embedded has the same one phase of data collection, but has a primary method that “guides the project and secondary database that is embedded, or nested, within” (214) the primary method. The embedding of the secondary database means that it either addresses a separate research question or “seeks information at a different level of analysis” (214). The concurrent transformative uses a specific theoretical perspective along with the concurrent data collection, but can use either the triangulation or embedded models in its design.

The approach to data analysis that caught my attention is data transformation. Why? I don’t know…The idea of having to “quantify the qualitative data” or to “qualify quantitative data’ intrigued me. I’m not entirely sure how you qualify quantitative data, but I think I’m probably overthinking it.

Since I am already over my word limit, AND since Dyson and Genishi was so straightforward I will keep my discussion of that book really short. Basically, to me the idea here is that “adults and children interpret their meanings in particular situations through interactions with others” (Dyson and Genishi 18). The role of the researcher is to use “methods of observation and analysis [of other people’s interactions] to understand other’s understandings” (Dyson and Genishi 12). In other words, the researcher interprets other people’s interpretations of meanings through observing their interactions with other people. Pretty simple...