• To understand scientific models and their inherent limitations and strengths when using the Empirical Test for Thematic Analysis
• To learn about scientific models developed from qualitative data and their mediating role between theory and data
• To discover the epistemological and methodological grounds needed to move thematic analysis beyond thematic maps and colourful illustrations to building and learning from models
Background Models are central to the acquisition and organisation of scientific knowledge. They can be viewed as tools for interpretive description as well as cognitive representations of an empirical phenomenon. However, discussions about how to develop models in qualitative research – particularly in the literature on thematic analysis – are sparse.
Aim To discuss an approach to scientific qualitative modelling that uses the new technique described in the first part of this article (Gildberg and Wilson 2023): the Empirical Test for Thematic Analysis (ETTA).
Discussion The authors discuss scientific models and their inherent limitations and strengths, so that others may assess models and their potential.
Conclusion A limitation of ETTA is the risk that excessive rigour and systematisation could reduce creativity in the construction of models. However, on balance there is a scientific need for qualitative researchers to improve their capability to refine and describe the techniques they use to construct models, adequately explain the reliable generation of models, and improve transparency regarding the epistemological and methodological basis for the construction of models.
Implications for practice By using ETTA on qualitative data obtained from clinical practice it becomes possible to illuminate the interconnections among themes within the data. This approach not only assists in illustrating these connections, it also enables clinicians and researchers to gain a comprehensive understanding of specific clinical phenomena through the use of models. The process of developing and using these models enables the simulation and strategic intervention development based on data that addresses the specific problem being investigated.
Nurse Researcher. doi: 10.7748/nr.2023.e1893Peer review
This article has been subject to external double-blind peer review and checked for plagiarism using automated softwareCorrespondence
Gildberg FA, Wilson R (2023) Scientific models for qualitative research: a textual thematic analysis coding system – part 2. Nurse Researcher. doi: 10.7748/nr.2023.e1893Acknowledgments
The authors would like to thank RD Nissen, JH Kerring, ALW Pedersen and colleagues for contributing with reflections, critical reading, and debates of this paper, from draft to finished manuscript. The sharp pen of Roman Frigg has not gone unnoticed in this process. This work is dedicated to the memory of D Bailer-Jones
Published online: 13 July 2023
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