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Leveraging Generative Text Models and Natural Language Processing to Perform Traditional Thematic Data Analysis

Isil Anakok, Andrew Katz, Kai Jun Chew, Holly Matusovich

International Journal of Qualitative Methods, 2025

Read the paper at the publisher DOI 10.1177/16094069251338898

Summary

This paper explores how generative text models and natural language processing can be leveraged to perform traditional thematic data analysis in educational research contexts.

Cite this paper

Anakok, I., Katz, A., Chew, K. J., & Matusovich, H. (2025). Leveraging Generative Text Models and Natural Language Processing to Perform Traditional Thematic Data Analysis. International Journal of Qualitative Methods. https://doi.org/10.1177/16094069251338898

BibTeX
@article{anakok2025leveraging,
  title = {Leveraging Generative Text Models and Natural Language Processing to Perform Traditional Thematic Data Analysis},
  author = {Anakok, Isil and Katz, Andrew and Chew, Kai Jun and Matusovich, Holly},
  journal = {International Journal of Qualitative Methods},
  year = {2025},
  doi = {10.1177/16094069251338898}
}

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