Analyzability

Research Integrity & a Total Quality Framework Approach to Qualitative Data Sharing

The September 2021 issue of Monitor on Psychology from the American Psychological Association includes an article “Leading the Charge to Address Research Misconduct” by Stephanie Pappas. The article discusses the various Qualitative data sharingcircumstances or “pressures” that may lead researchers towards weak research practices that result in anything from “honest” mistakes or errors (e.g., due to insufficient training or oversight) to deliberate “outright misconduct” (e.g., falsifying data, dropping outliers from the analysis and reporting). The article goes on to talk about what psychologists are doing to tackle the problem.

One of those psychologists is James DuBois, DSc, PhD at Washington University School of Medicine. Dr. DuBois and his colleague Alison Antes PhD direct the P.I. (professionalism and integrity in research) Program at Washington University. This program offers one-on-one coaching to researchers who are challenged by the demands of balancing scientific and compliance requirements, as well as researchers who have (or have staff who have) been investigated for noncompliance or misconduct. The P.I. Program also conducts an On the Road Workshop which is an onsite session for researchers “doing empirical research in funded research environments” covering such areas as decision-making strategies, effective communication, and professional growth goals.

Another approach to the problem of misconduct and the goal of research integrity is transparency by way of sharing data (and other elements of design), allowing other researchers the opportunity to examine research practices and substantiate the reported results. Dr. DuBois and his co-authors discuss this and other advantages to sharing qualitative data in their 2018 article “Is It Time to Share Qualitative Research Data?” The authors assert that allowing other researchers to assess supporting evidence and “comprehensiveness by examining our data may improve the quality of research by enabling correction and increasing attention to detail” (p. 384).

In response to DuBois et al., Roller and Lavrakas (2018) published a commentary expressing Read Full Text

Elevating Qualitative Design to Maximize Research Integrity

The following is a modified excerpt from Applied Qualitative Research Design: A Total Quality Framework Approach (Roller & Lavrakas, 2015, pp. 9-10).

All research that is aimed at understanding how people think and behave requires a principled approach to research design that is likely to maximize data quality and to instill users’ confidence in the research outcomes. This is no less so in qualitative than it is in quantitative research; and, in fact, the distinctive attributes and underlying complexities in qualitative research necessitate a quality approach to qualitative research design. This approach requires qualitative researchers to build certain principles into their research studies by way of incorporating and practicing fundamental research standards.

Total Quality FrameworkTo that end, the Total Quality Framework (TQF) was devised to provide a basis by which researchers can develop critical thinking skills necessary to the execution of qualitative designs that maximize the integrity of the research outcomes. This framework is not intended to prescribe a formula or specific procedure by which qualitative researchers should conduct qualitative inquiry. Rather, the TQF provides researchers with a flexible way to focus on quality issues, examine the sources of variability and possible bias in their qualitative methods, and incorporate features into their designs that mitigate these effects and maximize quality outcomes. Integral to the TQF is the idea that all qualitative research must be Credible, Analyzable, Transparent, and Useful. These four components are fundamental to the TQF and its ability to help researchers identify the strengths and limitations of their qualitative methods while also guiding them in the qualitative research design process.

By holding the quality of qualitative research design to a deep level of scrutiny when applied across the diverse, multidisciplinary fields utilizing qualitative methods — e.g., education; psychology; anthropology; sociology; nursing, public health, and medicine; communication; information management; business; geography and environmental science; and program evaluation — the discussion of qualitative research is significantly elevated and enables students, faculty, and practitioners to design and interpret qualitative research studies based on the quality standards that are the hallmark of the TQF.

 

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

Qualitative Analysis: A Reflexive Exercise for Category Development

The second component of the Total Quality Framework (TQF) is Analyzability. This component provides researchers with critical thinking considerations relevant to the completeness and accuracy of their analyses and interpretations of the data. Analyzability consists of two fundamental elements — processing and verification — the first of which involves coding followed by deriving categories and themes from the data.

From a TQF perspective, a useful exercise for category development — particularly when the study entails multiple researchers and a large amount of data — is by way of the reflexive template. Although similar in spirit to the writing function in computer-assisted qualitative data analysis software programs, the primary purpose of this reflexive template is to encourage researchers to actively reflect as they go about developing categories or buckets from the underlying constructs gained from the data. By way of the template, the analyst can document the relationship they perceive between the category and the construct as well as provide an example or further input to support their thinking.

For instance, a researcher conducting a qualitative content analysis study of diaries written by women confined to prison concerning their activities and experiences during confinement, may have derived the category “educational opportunity” (EDUOPPTY) from the coded data defined in part (i.e., along with other relevant constructs) by the underlying construct “well-being.” Within the well-being construct, the researcher also identified three key subconstructs — physical well-being, mental well-being, and financial well-being — that play a central role in understanding the meaning of the well-being construct as well as deepening the definition of the EDUOPPTY category. In this example, the reflexive exercise (by way of the template, see below) has facilitated the researcher’s ability to record the connections between the category and key constructs — highlighting instances of the relationship between EDUOPPTY (e.g., how to use the exercise equipment and art classes) and physical well-being, mental well-being, as well as financial well-being — while aiding collaboration with the research team and adding transparency to the analysis process.

Reflexive template for category development