Total Quality Framework

Applying the TQF Credibility Component: An IDI Case Study

The Total Quality Framework (TQF) is an approach to qualitative research design that integrates quality principles without stifling the fundamental and unique attributes of qualitative research. In so doing, the TQF helps qualitative researchers develop critical thinking skills by showing them how to give explicit attention to quality issues related to conceptualization, implementation, analysis, and reporting.

The following case study offers an example of how many of the concerns of the Credibility (or data collection) component of the TQF were applied to an in-depth interview (IDI) study conducted by Roller Research. This case study can be read in its entirety in Roller & Lavrakas (2015, pp. 100-103).

Credibility Component of the Total Quality FrameworkScope

This study was conducted for a large provider of information services associated with nonprofit organizations based in the U.S. The purpose was to investigate the information needs among current and former users of these information services in order to facilitate the development of “cutting edge” service concepts.

Eighty-six (86) IDIs were conducted among individuals within various grant-making and philanthropic organizations (e.g., private foundations, public charities, and education institutions) who are responsible for the decision to purchase and utilize these information services.

There were two important considerations in choosing to complete 86 interviews: (a) the required level of analysis – it was important to be able to analyze the data by the various types of organizations, and (b) practical considerations – the available budget (how much money there was to spend on the research) and time restrictions (the research findings were to be presented at an upcoming board meeting). In terms of mode, 28 IDIs were conducted with the largest, most complex users of these information services, while the remaining 58 interviews were conducted on the telephone.

Participants were stratified by type, size, and geographic location and then selected on an nth-name basis across the entire lists of users and former users provided by the research sponsor.

A high degree of cooperation was achieved during the recruitment process by way of: Read Full Text

The Important Role of “Buckets” in Qualitative Data Analysis

An earlier article in Research Design Review“Finding Connections & Making Sense of Qualitative Data” – discusses the idea that a quality approach to a qualitative research design incorporates a carefully considered plan for analyzing, and making sense of, the data in order to produce outcomes that are ultimately useful to the users of the research. Specifically, this article touches on the six recommended steps in the analysis process.* These steps might be thought of as a variation of the classic Braun & Clarke (2006) thematic analysis scheme in that the researcher begins by selecting a unit of analysis (and thus becoming familiar with the data) which is then followed by a coding process.

Unique to the six-step process outlined in the earlier RDR article is the step that comes after coding. Rather than immediately digging into the codes searching for themes, it is recommended that the researcher look through the codes to identify categories. These categories basically represent buckets of codes that are deemed to share a certain underlying construct or meaning. In the end, the researcher is left with any number of buckets filled with a few or many codes from which the researcher can identify patterns or themes in the data overall. Importantly, any of the codes within a category or bucket can (and probably will) be used to define more than one theme.

As an example, consider an in-depth interview study with financial managers of a large non-profit organization concerning their key considerations when selecting financial service providers. After the completion of 35 interviews, the researcher absorbs the content, selects the unit of analysis (the entire interview), and develops 75-100 descriptive codes. In the next phase of the process the researcher combs through the codes looking for participants’ thoughts/comments that convey similar broad meaning related to the research question(s). In doing so, Read Full Text

Mobile & Online Qualitative Research: The Good, the Bad, & the Ugly

Data quality matters. Regardless of the research method or approach, our ability to say anything meaningful about our research outcomes hinges on the integrity of the data. The greater care the researcher takes to ensure the basic ingredients of “good” research design, the more confident the researcher and importantly the user of the research will be in the recommendations drawn from the research and its ultimate usefulness.

This focus on data quality applies to all research. And although it is most often a topic of discussion among survey researchers, data quality considerations are increasingly (I hope!) a discussion among qualitative researchers as well. Indeed, the underlying validity of our qualitative data is an important consideration regardless of the researcher’s paradigm orientation or the qualitative method, including the more recent methodological options – that is, mobile and online qualitative research.

Mobile and online technology – in particular, tech solutions that combine observation with a multimethod/mode approach – offer qualitative researchers new ways to investigate a variety of situations that give them a closer understanding of participants’ lived experiences as never before possible. Three such situations are: Read Full Text