Among the 21 articles published in Research Design Review in 2018, five focused on a few fundamental aspects of the qualitative research approach. These articles address such topics as the meaning researchers give to qualitative research and whether researchers are really conducting qualitative “research” or uncovering qualitative “information.” These articles also include a discussion about the idea that a consideration of qualitative methods is separate from attention to paradigm orientation, and two articles are directed at what it means to be “literate” in qualitative research design and how rigor throughout the quality chain results in useful outcomes by way of new hypotheses, next steps, and/or applications to other contexts.
The compilation of these five articles is available for download by clicking on the title — “The Meaning & Essence of Qualitative Research: Five Articles from Research Design Review Published in 2018.” This is only one of many compilations that have been put together in the month of January since 2012 to cover articles published the previous year. In January 2018, for example, a compilation of 20 articles covering a wide assortment of topics in qualitative research was published. A similar compilation was put together in January 2017. In other years, the compilation includes articles pertaining to both qualitative and quantitative design, such as “Designing Research to Understand How People Think: The Bridge that Connects Quantitative & Qualitative Research” published in January 2014.
There is good reason to wonder what researchers mean when they talk about “qualitative research.” This is not a trite bemusement. Indeed, there is often an unspoken underlying premise in most discussions of “qualitative research” that researchers harbor a mutually agreed-to concept of what qualitative research is, when in fact this is not the case. Attend a qualitative research conference session and you will find that the presenter predictably delves into the particular subject matter without a hint of the researcher’s definition of “qualitative research,” leaving attendees with the arduous (and misguided) task of linking their own concept of qualitative research with the presenter’s discussion.
There are a number of ways that researchers may conceptualize or define qualitative research. For instance, some may define qualitative research simply by its unique set of methods, e.g., focus group discussions, in-depth interviews, ethnography; whereby, a focus group study is deemed qualitative research regardless of the skills of the moderator or how the data are treated or reported to end users. Similarly, qualitative research may be understood solely by the interview format, e.g., a semi-structured in-depth interview (IDI) constitutes qualitative research while a structured IDI not so much (and actually leans towards a more quantitative approach).
Another understanding of qualitative research may center on the intent or types of questions being asked. For example, I have heard quantitative researchers refer to their design decisions (such as weighing project costs with research quality) as qualitative research. And some researchers may think that any approach that is self-reflective in nature (such as autoethnography) is qualitative research. Some researchers also use labels Read Full Text
There is a significant hurdle that researchers face when considering the addition of qualitative methods to their research designs. This has to do with the analysis – making sense – of the qualitative data. One could argue that there are certainly other hurdles that lie ahead, such as those related to a quality approach to data collection, but the greatest perceived obstacle seems to reside in how to efficiently analyze qualitative outcomes. This means that researchers working in large organizations that hope to conduct many qualitative studies over the course of a year are looking for a relatively fast and inexpensive analysis solution compared to the traditionally more laborious thought-intensive efforts utilized by qualitative researchers.
Among these researchers, efficiency is defined in terms of speed and cost. And for these reasons they gravitate to text analytic programs and models powered by underlying algorithms. The core of modeling solutions – such as word2vec and topic modeling – rests on “training” text corpora to produce vectors or clusters of co-occurring words or topics. There are any number of programs that support these types of analytics, including those that incorporate data visualization functions that enable the researcher to see how words or topics congregate (or not), producing images such as these Read Full Text