The Total Quality Framework (TQF) has been discussed in several articles appearing in Research Design Review. Some of these articles simply reference the TQF in the context of a broader discussion while others – such as “A Quality Approach to the Qualitative Research Proposal” and “Evaluating Quality Standards in a Qualitative Research Literature Review” – speak more directly about applications of the TQF. The TQF is defined as “a comprehensive perspective for creating, managing, and interpreting quality research designs and evaluating the likelihood that a qualitative study will provide information that is valid and useful for the purposes for which the study is intended” (Roller & Lavrakas, 2015, pp. 21-22). In essence, the framework offers qualitative researchers a way to think about the quality of their research designs across qualitative methods as well as a particular paradigm or theoretical orientation. In this way, the TQF is grounded in the core belief that,

if it is agreed that qualitative research can, in fact, serve worthwhile purposes, then logically it would serve those purposes only to the degree that it is done well, regardless of the specific objectives that qualitative researchers strive to address. (p.20)

There are four components to the TQF – Credibility, Analyzability, Transparency, and Usefulness – each pertaining to a distinct aspect of the research process. The schematic (below) shows the interrelatedness of these components, with each of the first three components contributing to the fourth component, and ultimate goal of qualitative inquiry, i.e., Usefulness.

This article is a brief discussion of Credibility which is the TQF component having to do with data collection in qualitative research. Subsequent articles are devoted to the other three components – Analyzability, Transparency, and Usefulness.

From a TQF perspective, credible qualitative research is the result of effectively managing data collection, paying particular attention to the two specific areas of Scope and Data Gathering. Scope has to do with how well the participants from which data are gathered represent the broader population of people that is the focus of investigation. There are four considerations related to Scope. The qualitative researcher needs to think about^*: (a) defining the target population; (b) how these individuals will be selected for inclusion in the study (i.e., the source itself – e.g., a list to sample from, a community center to draw from – and the procedures to be used to sample from the source); (c) how many participants the researcher ultimately wants to include in the study; and (d) strategies to maximize the researcher’s ability to gain access to and cooperation from the people of interest.

There are articles in RDR that discuss the various considerations related to Scope. For example, a RDR post back in 2012 titled “Designing a Quality In-depth Interview Study: How Many Interviews Are Enough?” talked about the many factors researchers should think about when determining the number of in-depth interviews to complete for an IDI study, both at the initial design phase as well as when in the field.

Data Gathering is the other critical ingredient to Credibility. Data Gathering has to do with how well the data collected in a qualitative study accurately represent the concepts the study set out to investigate. Data Gathering, you might say, is concerned with construct validity (where “construct” may refer to anything from a narrow topic to a broad and possibly ambiguous concept), addressing the question of How confident am I that my data truly answer my research objectives? There are four considerations the qualitative researcher will want to think about when designing and conducting Data Gathering: (a) identifying the appropriate constructs – as well as the specific attributes within each construct – to measure based on the research question or objectives; (b) choosing the appropriate qualitative method as well as the appropriate mode; (c) developing the data collection tool(s) to effectively operationalize and measure the constructs and their attributes, e.g., the interview or discussion guide; and (d) mitigating sources of bias and inconsistency associated with the data collector (researcher) as well as the participants.

There are many examples in RDR of articles that discuss various considerations within Data Gathering. For example, the development of an interview guide is the topic of “Interview Guide Development: A 4-Stage ‘Funnel’ Approach.” And articles that address issues of researcher and/or participant bias and inconsistency include “The Recipe for Quality Outcomes in Qualitative Research Includes a Healthy Dose of Consistency,” “Mitigating Researcher-as-instrument Effects,” and “Qualitative Data: Achieving Accuracy in the Absence of ‘Truth’.”

Credible qualitative research is derived, not from a strict set of rules to follow but rather, from a keen sense of the research objectives and an understanding of how to think about the research principles that apply to data collection in relationship to the research question under investigation. By way of the TQF Credibility component, qualitative researchers are encouraged to think carefully about the composition (and inclusiveness) of their participants along with the unbiased and consistent manner in which data is gathered. It goes without saying that the flexible and contextual nature of qualitative research will attract any number of missteps – e.g., a skewed participant mix or researcher effects that bias the data – but the point here is that qualitative researchers need to be conscious of these factors, to reflect upon them and record these reflections, and to use this information in the interpretation and reporting of findings. This, of course, is where the other TQF components — Analyzability, Transparency, and Usefulness — play key roles.

 

^*These considerations also pertain to qualitative content analysis where the focus is on objects and text rather than individuals.

Roller, M. R., & Lavrakas, P. J. (2015). Applied qualitative research design: A total quality framework approach. New York: Guilford Press.