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Abstract

In this paper, we argue that qualitative data analysis software lacks a tool that can be used to fulfill an algorithmic evaluation of conceptualization carried out in qualitative studies. We propose the context-oriented models of coding that conjugate single codes, that is, brief denotations made in natural language, by unusual local relationships called context-fixed elucidation (CFE). CFE is a local relationship between miscellaneous aspects of a case under study. The set of separate CFEs, originated by the analyst during conceptualization and called thesaurus, represents the case as a whole. On the basis of CFE structure and using the thesaurus’ single codes as data, there is proposed an algorithm which calculates, without the involvement of the expert, whether there is or not global coherence of single codes used by analyst within the thesaurus. The tool thus obtained emulates for the codes originated in qualitative study the relationships known in the object-oriented programming, such as polymorphism, visibility, encapsulation, inheritance. A probe application of the new tool is demonstrated by the conceptualization of textual evidence. The application was performed with the help of a pilot computer package which architecture is based on the context-oriented models. Thanks to the models, QDAS can obtain special tools that would make researchers' analytical work more intelligible and coherent. The models proposed can find applications outside of research discourse including computer technologies used in various social spheres where people communicate in natural language.

Keywords

qualitative data, qualitative data analysis, context oriented methods, ontology, semantic networks

Author Bio(s)

Gennady Kanygin is the Leading Researcher at Sociological Institute of Russian Academy of Sciences - the branch of Federal Center of Theoretical and Applied Sociology of the Russian Academy of Sciences in Saint Petersburg. His methodological interests include computer methods in sociology, computer-aided interviewing, qualitative data analysis, and knowledge management. He has published in each of these areas. Please direct correspondence to g.kanygin@gmail.com. Viktoria Koretckaia is a Researcher at Sociological Institute of Russian Academy of Sciences - the branch of Federal Center of Theoretical and Applied Sociology of the Russian Academy of Sciences in St. Petersburg. She participates in several science and commercial research and development projects related to knowledge management systems. Her methodological interests include computer methods in sociology, knowledge management, databases’ structure engineering, machine learning, information systems engineering. She has published in Russian in the area of Knowledge Management. Please direct correspondence to interperfection@gmail.com.

Acknowledgements

We would like to express our gratitude to Yuta Tamberg, who helped us with the English text for this article and whose patience and active concern cannot be overestimated.

Publication Date

2-1-2021

Creative Commons License

Creative Commons Attribution-Noncommercial 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 4.0 License.

DOI

10.46743/2160-3715/2021.4342

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