Postprint: Scientific modeling in qualitative research: Accounting for scientific models generated through a textual thematic analysis coding system

Models are central to the acquisition and organization of scientific knowledge and can be viewed both as tools for interpretative description and as cognitive representations of an empirical phenomenon. However, descriptions about how to develop models within qualitative research, particularly within the literature on thematic analysis, is sparse. This article presents an approach to scientific qualitative modeling using a new technique: Empirical Test for Thematic Analysis (ETTA). We discuss the inherent limitations and strengths, so that others may assess its potential. Subsequently, an approach to the production of scientific qualitative models is presented. The method described highlights the importance of authenticity markings and taxonomical and functional semantic analysis, while the presented approach accounts for the generation of a semantic structure expressed through themecode, content and functionality. The primary advantage of the approach presented is the sequential need to account for taxonomic analysis, functionality factors, preconditioning items, cascade directories and modulation factors, which, in turn, result in the production of a sound systematic scientific development of a model.