Total Quality Framework

Quality Qualitative Research: As Strong As Its Weakest Link

The Total Quality Framework (TQF) is rooted in the idea that a quality approach to qualitative research requires “quality thinking” at each stage of the research process. It is an idea derived from the logic that it is not good enough to think carefully about data collection without also thinking as carefully about the analysis and reporting phases while keeping a discerning eye on the ultimate goal of gaining useful research results. This fundamental concept underlies the TQF and serves to define its four components – Credibility (pertaining to the data collection phase), Analyzability (analysis), and Transparency (reporting), and Usefulness (being able to do something of value with the outcomes).

By considering quality standards at each step in the research design, qualitative researchers maintain the integrity of their data through the entire study thereby producing something of value to the users of their research. For instance, a concerted quality approach to data collection – an approach that mitigates researcher bias and gathers valid data – but a disregard for the quality process in the analysis phase – e.g., transcripts are poorly done, coding is inconsistent, and verification of the data is absent – weakens the entire study. Likewise, a deliberate quality approach to data collection and analysis but a failure to write a transparent final document that reveals the details of the study’s scope, data gathering, analysis process and verification, effectively masks the integrity of the research and undermines its critical value to users.

A holistic quality-centric approach to qualitative research design essentially means that a weakness in any one link in the quality chain – the chain from data collection to analysis to reporting – erodes the purpose of conducting qualitative research (regardless of method) which is to offer useful information by way of new hypotheses, next steps, and/or applications to other contexts.

 

 

Image captured from: https://www.quora.com/Are-covalent-network-solids-stronger-than-ionic-bonds-in-regards-to-intermolecular-forces

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