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

The “Quality” in Qualitative Research Debate & the Total Quality Framework

The following is a modified excerpt from Applied Qualitative Research Design: A Total Quality Framework Approach (Roller & Lavrakas, 2015, pp.15-17).

The field of qualitative research has paid considerable attention in the past half century to the issue of research “quality.” Despite these efforts, there remains a lack of agreement among qualitative researchers about how quality Total Quality Frameworkshould be defined and how it should be evaluated (cf. Lincoln & Guba, 1985, 1986; Lincoln, 1995; Morse et al., 2002; Reynolds et al., 2011; Rolfe, 2006; Schwandt, Lincoln, & Guba, 2007). Some who seem to question whether quality can be defined and evaluated appear to hold the view that each qualitative research is so singularly unique in terms of how the data are created and how sense is made of these data that striving to assess quality is a wasted effort that never leads to a satisfying outcome about which agreement can be reached. Among other things, this suggests that validity – meaning, “the correctness or credibility of a description, conclusion, explanation, interpretation, or other sort of account” (Maxwell, 2013, p. 122) – is solely in the eye of the beholder and that convincing someone else that a qualitative study has generated valid and actionable findings is more an effort of subjective persuasion than an effort of applying dispassionate logic to whether the methods that were used to gather and analyze the data led to “valid enough” conclusions for the purpose(s) to which they were meant to serve.

Controversy also exists about how to determine the quality of a qualitative study. Arguments are made by some that the quality of a qualitative study is determined solely by the methods and processing that the researchers have used to conduct their studies. Others argue Read Full Text