The Science of Diabetes Self-Management and Care2024, Vol. 50(6) 441–443© The Author(s) 2024Article reuse guidelines:sagepub.com/journals-permissionsDOI: 10.1177/26350106241299714journals.sagepub.com/home/tde
Editor-in-Chief
The Science of Diabetes Self-Management and Care publishes high-quality, peer-reviewed research on the science of self-management related to diabetes and co-morbid conditions and for the growing body of knowledge related to technology, population health, and public health. Over the last couple of years, there has been an increase in qualitative research reports embraced across disciplines published in The Science of Diabetes Self-Management and Care, recognizing that qualitative methods provide deeper insights into complex human experiences that are often difficult to capture with quantitative methods alone. As with quantitative reports, scientific rigor is paramount to qualitative research and is critical in advancing health services and outcomes research. The process of planning, recruiting, data collection, and analysis is essential to the report. Equally important is how trustworthiness is implemented, a common term used in qualitative research to represent rigor. Issues of trustworthiness and transparency, using checklists when writing qualitative reports, and using an inductive approach to analyze qualitative research are essential in establishing confidence in the findings.
The terms’ reliability, validity, and generalizability are synonymous with quantitative research methods. The terms trustworthiness and transparency focus on how researchers engage in their research and write up study findings. Lincoln and Guba1 highlight four elements that need to be addressed in their approach to trustworthiness including credibility, transferability, dependability, and confirmability.
Credibility (competence and truth of the data, similar to internal validity) focuses on how plausible data analyses are and the interpretation. Credibility answers the question, “How congruent are the findings with reality?” “Are study findings hanging together?” An approach to promoting credibility is through the process of triangulation. Triangulation is the process of using several sources of information to establish identifiable patterns. Several types of triangulations exist (e.g., methodological, investigator, theoretical, and environmental), all of which test the credibility of one’s research. Another method of strengthening credibility is using “member checks.” Members refer to research participants in a qualitative research study. Once the initial data analysis process is complete, the researcher goes back to selected participants with variations in experiences to share the themes identified by the outsider to confirm the researcher’s interpretation was accurate and represents their experience. This strategy builds on the researcher’s prolonged engagement (another credibility strategy) in the phenomenon. The researcher spends much time with the interview participants, immersed in the experience. Having another researcher or team member read and react to field notes establishes a sense of trust and reality. Peer debriefing provides critical feedback before information gets published and is seen as an act of trust. Discussing with other health care providers specializing in the phenomenon of interest clinically and other researchers specialized in the specific phenomenon of interest can serve as an objective sounding board to ensure from the transcripts you are interpreting and describing accurately without biasing the experience.
Dependability (like reliability) refers to the stability of data analysis. It ensures the study could be replicated vis a vis the process of the method described. Along with the care and meticulous audit trail, another strategy of dependability is bracketing. Bracketing is the process of identifying and acknowledging the preconceptions, biases, and assumptions that researchers may bring to a study, especially during the interview and analysis process. It is critical to recognize that researcher bias and various assumptions are present and difficult to control when conducting research. It is, therefore, necessary for researchers to monitor their biases and assumptions. Allowing researchers to acknowledge their biases and experiences minimizes the influence of the researcher’s view on data analysis.
Confirmability (like objectivity) centers on verifying the phenomenon of interest using different data sources. Confirmability is maintained by an audit trail, which includes a record of decision rules in the analysis process. All raw data are also stored for purposes of replication or review if requested from external sources. Reflexivity, or being transparent and documenting any potential biases from the researcher’s/interviewer’s perspective, also helps the transparency process, which is critical to ensuring reliable and valid qualitative findings.
Transferability, like external validity, refers to how the researcher chose the sample and the descriptive characteristics of research participants. Transferring data provides a context for the results and the ability to decide if other study findings share common attributes. Transferability ensures that readers can follow the ‘findings’ logic, thus enhancing the possibility of broader understanding in other populations or settings. Transferability is possible when the raw data provides a rich and full description of the data, including participant quotes along with pertinent contextual information.2
Several articles highlight the use of comprehensive checklists to demonstrate rigorous qualitative research reporting. These checklists, such as the consolidated criteria for reporting qualitative research (COREQ)3 and the Standards for Reporting Qualitative Research (SBQR)4 cover a wide range of aspects in the research process. Other checklists from institutions like the Joanna Briggs Institute (JBI) or the American Psychological Association (APA) assess the rigor of qualitative research by evaluating aspects of the research design, sampling technique, data collection methods, data analysis process, researcher reflexivity, and the clarity of reporting. This comprehensive approach ensures the trustworthiness of study findings.
While checklists may assist researchers in conceptualizing and writing up study findings, they do not fit all methods. Assessing criteria for all qualitative approaches, including descriptive or interpretive designs, cannot be ensured by one checklist.5 Analyses that are acceptable for descriptive research designs may invalidate interpretive research designs. Using a checklist may also imply to others that the research is rigorous if all aspects of the checklist have been checked off and the researcher has included the statement in the research report. Per Braun and Clark,6‘checklists’ like the COREQ3 undermine the quality and rigor of qualitative research reports. Use of checklists make the qualitative process and write-up of study findings ‘neat’ versus the messiness of data that is typically part and parcel of doing qualitative research. The messiness allows for the contextual nuances of the phenomenon to be part of the description of the experience. There are no universal checklists.
Qualitative data analysis falls into one of two categories: inductive or deductive.7 Inductive analysis is the appropriate strategy to employ, allowing the generation of ideas, codes, and themes to emerge from the data. Inductive analysis is a ‘bottom-up’ approach where the researcher goes through data line by line, assigning codes to various sections of the transcript as themes and concepts unfold relevant to the research question(s). Findings arise directly from the analysis of raw data, not from a priori expectations or models. Deductive analysis refers to applying theory to the data where the researcher often applies predetermined codes to the theory based on the literature or theory. The deductive approach is more of a ‘top-down’ approach where the researcher uses preconceived codes and categories to see what part of the data fits those codes.
A rich thematic analysis is a detailed qualitative data analysis in which the researcher examines the data to identify recurring themes and patterns, including rich contextual details and direct participant quotes to provide an understanding of the phenomena. Thick thematic analysis is not just the identification of themes and patterns but a deeper dive into describing the context in which themes and patterns emerge. The purpose of rich thematic analysis is to capture the richness and complexity of the data through analysis. While thematic analysis breaks down and organizes raw data into codes, researchers must provide rich and detailed descriptions of the research context, capturing the full richness of meaning and nuances within the data. Researchers should also include detailed participant quotes and contextual information to provide a thorough understanding of the phenomenon.
With the number of qualitative research reports continuing to increase along with an increasing interest in qualitative inquiry, researchers need to pay attention when writing rigorous or trustworthy qualitative research. A couple of essential areas mentioned in this editorial when reporting the results of qualitative research include trustworthiness, the use of checklists in assessing the quality of qualitative research, and the use of an inductive approach when analyzing qualitative data. For a more detailed consideration of the rigor associated with writing qualitative research reports, refer to other published books and articles on the topic.
James A. Fain https://orcid.org/0000-0002-6287-1413
Lincoln YS, Guba EG. Naturalistic Inquiry. Sage; 1985.
Stahl NA, King JR. Expanding approaches for research: understanding and using trustworthiness in qualitative research. JDev Educ. 2020;44(1):26-28.
Tong A, Sainsbury P, Craig J. Consolidated criteria for reporting qualitative research (COREQ): a 32-item checklist for interviews and focus groups. Int J Qual Care. 2007;19(6):349-357. doi:10.1093/intqhc/mzm042
O’Brien BC, Harris IB, Beckman TJ, Reed DA, Cook DA. Standards for reporting qualitative research: a synthesis of recommendations. Acad Med. 2014;89(9):1245-1251. doi:10.1097/ACM.0000000000000388
Morse J. The changing face of qualitative inquiry. Int J Qual Methods. 2020;19:1-7.
Braun V, Clark V. How do you solve a problem like COREQ? A critique of Tong et al’s (2007) consolidated criteria for reporting qualitative research. Methods Psychol. 2024;11:100155. doi:10.1016/j.metip.2024.100155
Azungah T. Qualitative research: deductive and inductive approaches to data analysis. Qual Res J. 2018;18(4):383-400. doi:10/1108/QRJ-D-18-00035
