Frederik Alkier Gildberg Professor, Forensic Mental Health Research Unit, Middelfart, Faculty of Health Science, Department of Regional Health Research, University of Southern Denmark, Denmark
Rhonda Wilson Professor, University of Newcastle School of Nursing and Midwifery, Callaghan, NSW, Australia
Why you should read this article:

• To add a high degree of trustworthiness and rigour to your use of thematic analysis

• To discover how to move your thematic analysis beyond thematic maps and colourful illustrations, to building and learning from models

• To learn how to rigorously account for scientific models developed from qualitative data

Background Models are central to the acquisition and organisation of scientific knowledge. However, there are few explanations of how to develop models in qualitative research, particularly in terms of thematic analysis.

Aim To describe a new technique for scientific qualitative modelling: the Empirical Testing Thematic Analysis (ETTA). Part 2 describes the ETTA model.

Discussion ETTA generates a semantic structure expressed through theme-code, content and functionality. It highlights the importance of authenticity markings and taxonomical and functional semantic analysis. Its primary advantage is the sequential need to account for taxonomic analysis, functionality factors, preconditioning items, cascade directories and modulation factors; this results in the production of a sound, systematic, scientific development of a model.

Conclusion ETTA is useful for nurse researchers undertaking qualitative research who want to construct models derived from their investigations.

Implications for practice This article provides a step-by-step approach for researchers undertaking research that culminates in the construction of a model derived from qualitative investigations.

Nurse Researcher. 31, 3, 30-35. doi: 10.7748/nr.2023.e1860

Correspondence

fgildberg@health.sdu.dk

Peer review

This article has been subject to external double-blind peer review and checked for plagiarism using automated software

Conflict of interest

None declared

Acknowledgements

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, all the way 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.

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