Abstract
This January 2024 issue of the Journal of Mixed Methods Research (JMMR) includes a tribute, an editorial and four articles.
Sadly, this issue includes a new tribute, in this case in memory of Pierre Pluye. Pierre passed away in August, 2023. Our sincere condolences to his family and friends. His colleagues Hong et al. (2024) provide this tribute honoring his legacy and contributions in the fields of mixed methods and primary care. They also highlight his role as mentor of many students. Pierre Pluye contributed to the JMMR as a member of the editorial board, author, and reviewer. We encourage JMMR readers to read this tribute.
In the editorial of this issue, Guetterman et al. (2024) discussed the importance of rhetoric and writing precisely about mixed methods. They focus on terminology and language and call for a common set of terms used in mixed methods research, including the term mixed methods itself. Terms and jargon should be well-defined at first use, and the growing consensus in terms is important for the maturity of mixed methods research as a field.
Regarding the four articles published in this January 2024 issue, in the first article, James et al. (2024), with affiliation in family medicine, presented a method of integrating transformative sampling considerations in explanatory sequential designs through a participant selection joint display. They conducted a study in the health sciences, examining emergency department utilization among deaf and hard-of-hearing patients. This study was designed using a community-engaged research approach. As indicated by the authors, this paper contributes to mixed methods research centering the transformative paradigm throughout a mixed methods study, including transformative considerations in joint displays, providing a structure to improve reporting in mixed methods community-based participatory research, and increasing transparency in mixed methods reporting.
In the second article, Gauly et al. (2024), with affiliations in health sciences and transport, described the development of a new integration method: the extended pillar integration process. The authors developed this integration method building on an existing approach known as pillar integration process. The new method contributes to the field of mixed methods providing steps on how to integrate data from more than two data sources. The authors offered detailed step-by-step guidance to use this method in a transparent and replicable way, providing two examples from two different disciplines: health sciences and automotive human factors.
In the third article, Sripathi et al. (2024), with affiliations in molecular genetics, biology, education, microbiology, and biochemistry, described their mixed methods approach to integrating qualitative analysis with predictive machine learning models. The authors examined how qualitative methods and machine learning for the analysis of short, student-written explanations are integrated to revise coding rubrics, improve human coding, and generate machine learning categorization models within the context of college science assessment. As indicated by the authors, recent advances in machine learning facilitate their mixed methods approach, using natural language processing for text analysis. The authors developed a two-stage mixed methods development of an automated scoring model, with a first stage in which the authors conducted an exploratory sequential design of model development and a second stage with a complex design of model revision.
In the fourth article, González Canché (2024), with affiliation in education, introduced a methodology called graphical retrieval and analysis of temporal information systems (GRATIS), and open-access software designed to visualize and analyze the time-based richness embedded in all qualitative and textual data. GRATIS employs dynamic network visualizations and data science mining tools. As indicated by the author, this methodology can contribute to mixed methods by focusing on temporal data analysis and visualization, integrating quantitative and qualitative methods to map participants’ contributions.
