There are four components to the Total Quality Framework in qualitative research design. The first component, Credibility, has to do with data collection; specifically, the completeness and accuracy of the data collected. There are two critical facets to Credibility – Scope (coverage and representation) and Data Gathering (bias, nonresponse, and how well [or not] particular constructs are measured).
The second component is Analyzability. This component is concerned with the completeness and accuracy of the analyses and interpretations. The Analyzability component is concerned with Processing (e.g., the use of transcriptions, coding) and Verification (e.g., by way of triangulation, deviant cases, and/or a reflexive journal).
By looking at just these two components of the TQF, what judgements can we make as to the strengths and limitations of the various modes 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
Observational research is “successful” to the extent that it satisfies the research objectives by capturing relevant events and participants along with the constructs of interest. Fortunately, there are two tools – the observation guide and the observation grid – that serve to keep the observer on track towards these objectives and generally facilitate the ethnographic data gathering process.
Not unlike the outlines interviewers and moderators use to help steer the course of their in-depth interviews and group discussions, the observation guide serves two important purposes: 1) It reminds the observer of the key points of observation as well as the topics of interest associated with each, and 2) It acts as the impetus for a reflexive exercise in which the observer can reflect on his/her own relationship and contribution to the observed at any moment in time (e.g., how the observer was affected by the observations). An observation guide is an important tool regardless of the observer’s role. For each of the five observer roles* – nonparticipant (off-site or on-site) and participant (passive, participant-observer, or complete) observation – the observation guide helps to maintain the observer’s focus while also giving the observer leeway to reflect on the particular context associated with each site.
As an adjunct to the observation guide, it is recommended that ethnographic researchers also utilize an observation grid. The grid is similar to the guide in that it helps remind the observer of the events and issues of most import; however, unlike the guide, the observation grid is a spreadsheet or log of sorts that enables the observer to actually record (and record his/her own reflections of) observable events in relationship to the constructs of interest. The grid might show, for instance, the relevant constructs or research issues as column headings and the specific foci of observation as rows. In an observational study of train travel, for example, the three key research issues related to activity at the train station might be: waiting for departures, delays in departures, and boarding; and the key areas of observation would pertain to behavior, conversations heard, and contextual information such as the weather and the general mood. Like the guide, the observation grid not only ensures that the principal issues and components are captured but also encourages the observer to reflect on each aspect of his/her observations and identify the particular ways the observer is influencing (or is being influenced by) the recorded observations.
*Roller & Lavrakas, 2015. Applied Qualitative Research Design: A Total Quality Framework Approach. New York: Guilford Press.