Development of a software method to assist in the thematic analysis of responses to open ended questions in Spanish-language surveys

ilustraciones, diagramas

Autores:: Cañas Palomino, Luis Alfonso

Tipo de recurso:

Fecha de publicación:: 2023

Institución:: Universidad Nacional de Colombia

Repositorio:: Universidad Nacional de Colombia

Idioma:: eng

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repository_id_str
dc.title.eng.fl_str_mv	Development of a software method to assist in the thematic analysis of responses to open ended questions in Spanish-language surveys
dc.title.translated.spa.fl_str_mv	Desarrollo de un método de software para asistir en el análisis temático de respuestas a preguntas abiertas en encuestas en español
title	Development of a software method to assist in the thematic analysis of responses to open ended questions in Spanish-language surveys
spellingShingle	Development of a software method to assist in the thematic analysis of responses to open ended questions in Spanish-language surveys 000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores 000 - Ciencias de la computación, información y obras generales::005 - Programación, programas, datos de computación Medición de software Software measurement Software metrics Thematic Analysis Qualitative Research Spanish-language Surveys Natural Language Processing (NLP) Multi-label Classification Zero-Shot Classification Análisis Temático Investigación Cualitativa Encuestas en Español Procesamiento del Lenguaje Natural (PLN) Clasificación Multi-etiqueta Clasificación Zero-Shot
title_short	Development of a software method to assist in the thematic analysis of responses to open ended questions in Spanish-language surveys
title_full	Development of a software method to assist in the thematic analysis of responses to open ended questions in Spanish-language surveys
title_fullStr	Development of a software method to assist in the thematic analysis of responses to open ended questions in Spanish-language surveys
title_full_unstemmed	Development of a software method to assist in the thematic analysis of responses to open ended questions in Spanish-language surveys
title_sort	Development of a software method to assist in the thematic analysis of responses to open ended questions in Spanish-language surveys
dc.creator.fl_str_mv	Cañas Palomino, Luis Alfonso
dc.contributor.advisor.none.fl_str_mv	Restrepo Calle, Felipe
dc.contributor.author.none.fl_str_mv	Cañas Palomino, Luis Alfonso
dc.contributor.researchgroup.spa.fl_str_mv	Plas Programming languages And Systems
dc.subject.ddc.spa.fl_str_mv	000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores 000 - Ciencias de la computación, información y obras generales::005 - Programación, programas, datos de computación
topic	000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores 000 - Ciencias de la computación, información y obras generales::005 - Programación, programas, datos de computación Medición de software Software measurement Software metrics Thematic Analysis Qualitative Research Spanish-language Surveys Natural Language Processing (NLP) Multi-label Classification Zero-Shot Classification Análisis Temático Investigación Cualitativa Encuestas en Español Procesamiento del Lenguaje Natural (PLN) Clasificación Multi-etiqueta Clasificación Zero-Shot
dc.subject.lemb.Spa.fl_str_mv	Medición de software
dc.subject.lemb.eng.fl_str_mv	Software measurement Software metrics
dc.subject.proposal.eng.fl_str_mv	Thematic Analysis Qualitative Research Spanish-language Surveys Natural Language Processing (NLP) Multi-label Classification Zero-Shot Classification
dc.subject.proposal.spa.fl_str_mv	Análisis Temático Investigación Cualitativa Encuestas en Español Procesamiento del Lenguaje Natural (PLN) Clasificación Multi-etiqueta Clasificación Zero-Shot
description	ilustraciones, diagramas
publishDate	2023
dc.date.issued.none.fl_str_mv	2023-12
dc.date.accessioned.none.fl_str_mv	2024-02-06T19:57:26Z
dc.date.available.none.fl_str_mv	2024-02-06T19:57:26Z
dc.type.spa.fl_str_mv	Trabajo de grado - Maestría
dc.type.driver.spa.fl_str_mv	info:eu-repo/semantics/masterThesis
dc.type.version.spa.fl_str_mv	info:eu-repo/semantics/acceptedVersion
dc.type.content.spa.fl_str_mv	Text
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dc.identifier.instname.spa.fl_str_mv	Universidad Nacional de Colombia
dc.identifier.reponame.spa.fl_str_mv	Repositorio Institucional Universidad Nacional de Colombia
dc.identifier.repourl.spa.fl_str_mv	https://repositorio.unal.edu.co/
url	https://repositorio.unal.edu.co/handle/unal/85634 https://repositorio.unal.edu.co/
identifier_str_mv	Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia
dc.language.iso.spa.fl_str_mv	eng
language	eng
dc.relation.references.spa.fl_str_mv	Aggarwal, C. C., & Zhai, C. (2012). Mining text data. Springer. https://doi.org/10.1007/978-1-4614- 3223-4 Akinepally, P. R. (2020). Investigating performance of different models at short text topic modelling. DEGREE PROJECT IN TECHNOLOGY. https : / / urn . kb . se / resolve ? urn = urn : nbn : se : kth : diva - 288531 Anfara, V. A., Brown, K. M., & Mangione, T. L. (2002). Qualitative analysis on stage: Making the research process more public. http://dx.doi.org/10.3102/0013189X031007028, 31, 28–38. https: //doi.org/10.3102/0013189X031007028 Archer, E. (2018). Qualitative data analysis: A primer on core approaches. ATLAS.ti Scientific Software Development GmbH. (2023). The qualitative data analysis & research software [Available at: https://atlasti.com/, Accessed: 2023-07-04]. Baumgartner, P., Smith, A., Olmsted, M., & Ohse, D. (2021). A framework for using machine learning to support qualitative data coding. OSF Preprints. https://doi.org/10.31219/OSF.IO/FUEYJ Bengtsson, M. (2016). How to plan and perform a qualitative study using content analysis. NursingPlus Open, 2, 8–14. https://doi.org/10.1016/J.NPLS.2016.01.001 Boog, B. (2005). Qualitative research practice. J. Soc. Interv. Theory Pract., 14(2), 47. Braun, V., Clarke, V., Hayfield, N., & Terry, G. (2019). Thematic analysis. In P. Liamputtong (Ed.), Handbook of research methods in health social sciences (pp. 843–860). Springer Singapore. https: //doi.org/10.1007/978-981-10-5251-4_103 Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D. M., Wu, J., Winter, C., … Amodei, D. (2020). Language models are few-shot learners. Bryman, A. (2004). Social research strategies. Social Research Methods, 3–25. Cañas, L. (2023). Thematic Analysis code snippets (Version 1.0.0). https : / / github . com / luis11181 /Thematic-Analisys Cer, D., Yang, Y., Kong, S.-y., Hua, N., Limtiaco, N., John, R. S., Constant, N., Guajardo-Céspedes, M., Yuan, S., Tar, C., Sung, Y.-H., Strope, B., & View, R. K. G. R. M. (2018). Universal sentence encoder. https://arxiv.org/abs/1803.11175v2 Crowston, K., Liu, X., & Allen, E. E. (2010). Machine learning and rule-based automated coding of qualitative data. Proceedings of the 73rd ASIS&T Annual Meeting on Navigating Streams in an Information Ecosystem - Volume 47. Czum, J. M. (2020). Dive into deep learning. Journal of the American College of Radiology, 17, 637–638. https://doi.org/10.1016/j.jacr.2020.02.005 Fearon, D. (2022). Qualitative data analysis software (qdas) overview - qualitative data analysis software (nvivo, atlas.ti, and more) - guides at johns hopkins university [Available at: https : / / guides.library.jhu.edu/QDAS Accessed: 2023-07-04]. Fri, C., & Elouahbi, R. (2020). Machine learning and deep learning applications in e-learning systems: A literature survey using topic modeling approach. Colloquium in Information Science and Technology, CIST, 2020-June, 267–273. https://doi.org/10.1109/CIST49399.2021.9357253 Gamieldien, Y., Case, J. M., & Katz, A. (2023). Advancing qualitative analysis: An exploration of the potential of generative AI and NLP in thematic coding. SSRN Electron. J. García, A. Z. (2021). Análisis de textos mediante técnicas nlp para la categorización de usuarios. http: //hdl.handle.net/10317/9647 Gasparetto, A., Marcuzzo, M., Zangari, A., & Albarelli, A. (2022). A survey on text classification algorithms: From text to predictions. Information 2022, Vol. 13, Page 83, 13, 83. https://doi.org/ 10.3390/INFO13020083 Gauthier, R. P., & Wallace, J. R. (2022). The computational thematic analysis toolkit. Proceedings of the ACM on Human-Computer Interaction, 6, 15. https://doi.org/10.1145/3492844 Graesser, A. C., & McNamara, D. S. (2012). Automated analysis of essays and open-ended verbal responses. Grimmer, J., & Stewart, B. M. (2013). Text as data: The promise and pitfalls of automatic content analysis methods for political texts. Political Analysis, 21(3), 267–297. https://doi.org/10. 1093/pan/mps028 Haj-Yahia, Z., Sieg, A., & Deleris, L. A. (2019). Towards unsupervised text classification leveraging experts and word embeddings. ACL 2019 - 57th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference, 371–379. https://doi.org/10.18653/V1/P19-1036 Hoxtell, A. (2019). Automation of qualitative content analysis: A proposal. Forum Qualitative Sozialforschung / Forum: Qualitative Social Research, 20. https://doi.org/10.17169/FQS-20.3.3340 Jelodar, H., Wang, Y., Yuan, C., Feng, X., Jiang, X., Li, Y., & Zhao, L. (2019). Latent dirichlet allocation (lda) and topic modeling: Models, applications, a survey. Multimedia Tools and Applications, 78, 15169–15211. https://doi.org/10.1007/S11042-018-6894-4/METRICS Lennon, R. P., Fraleigh, R., van Scoy, L. J., Keshaviah, A., Hu, X. C., Snyder, B. L., Miller, E. L., Calo, W. A., Zgierska, A. E., & Griffin, C. (2021). Developing and testing an automated qualitative assistant (aqua) to support qualitative analysis. Family medicine and community health, 9. https: //doi.org/10.1136/FMCH-2021-001287 Lester, J. N., Cho, Y., & Lochmiller, C. R. (2020). Learning to do qualitative data analysis: A starting point. https://doi.org/10.1177/1534484320903890, 19, 94–106. https : / / doi . org / 10 . 1177 / 1534484320903890 Li, Q., Peng, H., Li, J., Xia, C., Yang, R., Sun, L., Yu, P. S., & He, L. (2022). A survey on text classification: From traditional to deep learning. ACM Transactions on Intelligent Systems and Technology (TIST), 13, 31. https://doi.org/10.1145/3495162 Li, Y., Shyr, C., Borycki, E. M., & Kushniruk, A. W. (2021). Automated thematic analysis of health information technology (hit) related incident reports. Knowledge Management & E-Learning: An International Journal, 13, 408–420. https://doi.org/10.34105/J.KMEL.2021.13.022 Macey, W. H., & Fink, A. A. (2020). Employee Surveys and Sensing: Challenges and Opportunities. Oxford University Press. https://doi.org/10.1093/oso/9780190939717.001.0001 Mielke, S. J., Alyafeai, Z., Salesky, E., Raffel, C., Dey, M., Gallé, M., Raja, A., Si, C., Lee, W. Y., Sagot, B., & Tan, S. (2021). Between words and characters: A brief history of open-vocabulary modeling and tokenization in nlp. https://arxiv.org/abs/2112.10508v1 Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient estimation of word representations in vector space. 1st International Conference on Learning Representations, ICLR 2013 - Workshop Track Proceedings. https://arxiv.org/abs/1301.3781v3 Nanda, G., Jaiswal, A., Castellanos, H., Zhou, Y., Choi, A., & Magana, A. (2023). Evaluating the coverage and depth of latent dirichlet allocation topic model in comparison with human coding of qualitative data: The case of education research. Machine Learning and Knowledge Extraction, 5, 473–490. https://doi.org/10.3390/make5020029 Niedbalski, J., & Ślęzak, I. (2017). Computer assisted qualitative data analysis software. using the nvivo and atlas.ti in the research projects based on the methodology of grounded theory. Studies in Systems, Decision and Control, 71, 85–94. https : / / doi . org / 10 . 1007 / 978 - 3 - 319 - 43271-7_8/COVER OpenAI. (2023a). Gpt-4 technical report (Technical Report) [arXiv:2303.08774v3 [cs.CL]]. OpenAI. https://doi.org/10.48550/arXiv.2303.08774 OpenAI. (2023b). Models overview [Accessed: October 15, 2023]. https://platform.openai.com/docs/ models/overview Pennington, J., Socher, R., & Manning, C. D. (2014). Glove: Global vectors for word representation. EMNLP 2014 - 2014 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference, 1532–1543. https://doi.org/10.3115/V1/D14-1162 Pietsch, A. S., & Lessmann, S. (2019). Topic modeling for analyzing open-ended survey responses. https://doi.org/10.1080/2573234X.2019.1590131, 1, 93–116. https : / / doi . org / 10 . 1080 / 2573234X.2019.1590131 Puri, R., & Catanzaro, B. (2019). Zero-shot text classification with generative language models. arXiv preprint arXiv:1912.10165. QSR International Pty Ltd. (2021). Nvivo qualitative data analysis software [Available at: https : / / www.qsrinternational.com/nvivoqualitative-data-analysis-software/home Accessed: 2023- 07-04]. Radford, A., Narasimhan, K., Salimans, T., & Sutskever, I. (2018). Improving language understanding by generative pre-training. Reimers, N., & Gurevych, I. (2019). Sentence-bert: Sentence embeddings using siamese bert-networks. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing. http:// arxiv.org/abs/1908.10084 Restrepo-Calle, F., Ramírez-Echeverry, J., & Gonzalez, F. (2018). UNCode: Interactive System for Learning and Automatic Evaluation of Computer Programming Skills. EDULEARN18 Proceedings, 6888– 6898. https://doi.org/10.21125/edulearn.2018.1632 Restrepo-Calle, F., Ramírez-Echeverry, J. J., & González, F. A. (2020). Using an Interactive Software Tool for the Formative and Summative Evaluation in a Computer Programming Course: an Experience Report. Global Journal of Engineering Education, 22(3), 174–185. Rietz, T., & Maedche, A. (2021). Cody: An ai-based system to semi-automate coding for qalitative research. Conference on Human Factors in Computing Systems - Proceedings. https://doi.org/10. 1145/3411764.3445591 Rousseeuw, P. (1987). Rousseeuw, p.j.: Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. comput. appl. math. 20, 53-65. Journal of Computational and Applied Mathematics, 20, 53–65. https://doi.org/10.1016/0377-0427(87)90125-7 Safjan, K. (2021). Understanding micro and macro averages in multiclass multilabel problems. Krystian’s Safjan Blog. Saravia, E. (2022). Prompt Engineering Guide. https://github.com/dair-ai/Prompt-Engineering-Guide. Schopf, T., Braun, D., & Matthes, F. (2022). Lbl2vec: An embedding-based approach for unsupervised document retrieval on predefined topics. International Conference on Web Information Systems and Technologies, WEBIST - Proceedings, 2021-October, 124–132. https : / / doi . org / 10 . 5220 / 0010710300003058 Schouten, K., Frasincar, F., & de Jong, F. (2017). Ontology-enhanced aspect-based sentiment analysis. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10360 LNCS, 302–320. https://doi.org/10.1007/978-3-319- 60131-1_17/TABLES/6 Soratto, J., de Pires, D. E. P., & Friese, S. (2020). Thematic content analysis using atlas.ti software: Potentialities for researchs in health. Revista brasileira de enfermagem, 73, e20190250. https: //doi.org/10.1590/0034-7167-2019-0250 Stammbach, D., & Ash, E. (2021). Docscan: Unsupervised text classification via learning from neighbors. KONVENS 2022 - Proceedings of the 18th Conference on Natural Language Processing, 21–28. https://arxiv.org/abs/2105.04024v3 Tinsley, H. E., & Weiss, D. J. (1975). Interrater reliability and agreement of subjective judgments. Journal of Counseling Psychology, 22(4), 358. VERBI Software MAXQDA. (2023). All-in-one qualitative analysis software developed by and for researchers [Available at: https://www.maxqda.com/qualitative-analysis-software, Accessed: 2023-07-04]. Wang, W., Wei, F., Dong, L., Bao, H., Yang, N., & Zhou, M. (2020). Minilm: Deep self-attention distillation for task-agnostic compression of pre-trained transformers Wei, J., Bosma, M., Zhao, V., Guu, K., Yu, A. W., Lester, B., Du, N., Dai, A. M., & Le, Q. V. (2022). Finetuned language models are zero-shot learners. International Conference on Learning Representations. https://openreview.net/forum?id=gEZrGCozdqR Yao, S., Yu, D., Zhao, J., Shafran, I., Griffiths, T., Cao, Y., & Narasimhan, K. (2023). Tree of thoughts: Deliberate problem solving with large language models.
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dc.publisher.spa.fl_str_mv	Universidad Nacional de Colombia
dc.publisher.program.spa.fl_str_mv	Bogotá - Ingeniería - Maestría en Ingeniería - Ingeniería de Sistemas y Computación
dc.publisher.faculty.spa.fl_str_mv	Facultad de Ingeniería
dc.publisher.place.spa.fl_str_mv	Bogotá, Colombia
dc.publisher.branch.spa.fl_str_mv	Universidad Nacional de Colombia - Sede Bogotá
institution	Universidad Nacional de Colombia
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spelling	Reconocimiento 4.0 Internacionalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Restrepo Calle, Felipe82117c6c71f31211f86863049b600db3Cañas Palomino, Luis Alfonsoa2e3e2e8d6aebba1240d08e58532e3eaPlas Programming languages And Systems2024-02-06T19:57:26Z2024-02-06T19:57:26Z2023-12https://repositorio.unal.edu.co/handle/unal/85634Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, diagramasThematic analysis is fundamental in qualitative research, providing rich insights but often requiring substantial time and expertise. This work addresses some limitations of existing Computer-Assisted Qualitative Data Analysis Software (CAQDAS) and presents a novel method specifically designed to assist in the thematic analysis of multi-label open-ended questions in Spanish-language surveys. The proposed method melds domain expertise with advanced language models to establish preliminary categories. Subsequently, human discernment is combined with similarity measures to streamline the categorization of some responses using these preliminary categories. The process culminates in a robust and scalable automated categorization, utilizing diverse models, language models, and accuracy metrics. The proposed method is composed of three modular phases that can function independently or collaboratively, offering a comprehensive solution for researchers. It can reduce the labor-intensive coding process by leveraging Large Language Models (LLMs) and Natural Language Processing (NLP) techniques. The method's efficacy is evaluated through its application on a dataset from the National University of Colombia, demonstrating promising results across its various modules and pathways. The work opens avenues for further research, particularly in enhancing qualitative analysis methods with the integration of modern tools. (Texto tomado de la fuente)El análisis temático es fundamental en la investigación cualitativa, ofreciendo ideas valiosas pero a menudo requiriendo una cantidad significativa de tiempo y experiencia. Este trabajo aborda algunas limitaciones de los Software Asistidos por Computadora para el Análisis de Datos Cualitativos existentes y presenta un método novedoso diseñado específicamente para asistir en el análisis temático de preguntas abiertas con múltiples etiquetas para encuestas en español. El método propuesto combina la experiencia de dominio con modelos de lenguaje avanzados para establecer categorías preliminares. Posteriormente, el discernimiento humano se combina con medidas de similitud para agilizar la categorización de algunas respuestas utilizando estas categorías preliminares. El proceso culmina en una categorización automatizada robusta y escalable, utilizando diversos modelos, modelos de lenguaje y métricas de precisión. El método propuesto se compone de tres fases modulares que pueden funcionar de manera independiente o colaborativa, ofreciendo una solución integral a los investigadores. Puede reducir el largo proceso de codificación manual aprovechando los Grandes Modelos de Lenguaje (LLMs) y técnicas de Procesamiento de Lenguaje Natural (PLN). La eficacia del método se evalúa a través de su aplicación en un conjunto de datos de la Universidad Nacional de Colombia, mostrando resultados prometedores a través de sus diversos módulos y opciones. El trabajo abre vías para futuras investigaciones, particularmente en la mejora de los métodos de análisis cualitativos con la integración de herramientas modernas.MaestríaMagíster en Ingeniería - Ingeniería de Sistemas y ComputaciónComputación Aplicadaxv, 60 páginasapplication/pdfengUniversidad Nacional de ColombiaBogotá - Ingeniería - Maestría en Ingeniería - Ingeniería de Sistemas y ComputaciónFacultad de IngenieríaBogotá, ColombiaUniversidad Nacional de Colombia - Sede Bogotá000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores000 - Ciencias de la computación, información y obras generales::005 - Programación, programas, datos de computaciónMedición de softwareSoftware measurementSoftware metricsThematic AnalysisQualitative ResearchSpanish-language SurveysNatural Language Processing (NLP)Multi-label ClassificationZero-Shot ClassificationAnálisis TemáticoInvestigación CualitativaEncuestas en EspañolProcesamiento del Lenguaje Natural (PLN)Clasificación Multi-etiquetaClasificación Zero-ShotDevelopment of a software method to assist in the thematic analysis of responses to open ended questions in Spanish-language surveysDesarrollo de un método de software para asistir en el análisis temático de respuestas a preguntas abiertas en encuestas en españolTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMAggarwal, C. C., & Zhai, C. (2012). Mining text data. Springer. https://doi.org/10.1007/978-1-4614- 3223-4Akinepally, P. R. (2020). Investigating performance of different models at short text topic modelling. DEGREE PROJECT IN TECHNOLOGY. https : / / urn . kb . se / resolve ? urn = urn : nbn : se : kth : diva - 288531Anfara, V. A., Brown, K. M., & Mangione, T. L. (2002). Qualitative analysis on stage: Making the research process more public. http://dx.doi.org/10.3102/0013189X031007028, 31, 28–38. https: //doi.org/10.3102/0013189X031007028Archer, E. (2018). Qualitative data analysis: A primer on core approaches.ATLAS.ti Scientific Software Development GmbH. (2023). The qualitative data analysis & research software [Available at: https://atlasti.com/, Accessed: 2023-07-04].Baumgartner, P., Smith, A., Olmsted, M., & Ohse, D. (2021). A framework for using machine learning to support qualitative data coding. OSF Preprints. https://doi.org/10.31219/OSF.IO/FUEYJBengtsson, M. (2016). How to plan and perform a qualitative study using content analysis. NursingPlus Open, 2, 8–14. https://doi.org/10.1016/J.NPLS.2016.01.001Boog, B. (2005). Qualitative research practice. J. Soc. Interv. Theory Pract., 14(2), 47.Braun, V., Clarke, V., Hayfield, N., & Terry, G. (2019). Thematic analysis. In P. Liamputtong (Ed.), Handbook of research methods in health social sciences (pp. 843–860). Springer Singapore. https: //doi.org/10.1007/978-981-10-5251-4_103Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D. M., Wu, J., Winter, C., … Amodei, D. (2020). Language models are few-shot learners.Bryman, A. (2004). Social research strategies. Social Research Methods, 3–25.Cañas, L. (2023). 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Tree of thoughts: Deliberate problem solving with large language models.EstudiantesInvestigadoresMaestrosMedios de comunicaciónPúblico generalLICENSElicense.txtlicense.txttext/plain; charset=utf-85879https://repositorio.unal.edu.co/bitstream/unal/85634/1/license.txteb34b1cf90b7e1103fc9dfd26be24b4aMD51ORIGINAL1018491224.2024.pdf1018491224.2024.pdfTesis de Maestría en Ingeniería - Ingeniería de Sistemas y Computaciónapplication/pdf992076https://repositorio.unal.edu.co/bitstream/unal/85634/2/1018491224.2024.pdfd0794e459a826c40af829c26df74a532MD52THUMBNAIL1018491224.2024.pdf.jpg1018491224.2024.pdf.jpgGenerated Thumbnailimage/jpeg5005https://repositorio.unal.edu.co/bitstream/unal/85634/3/1018491224.2024.pdf.jpgecb162b1c00b5cae86fd8fb367a5c2bdMD53unal/85634oai:repositorio.unal.edu.co:unal/856342024-02-06 23:03:46.995Repositorio Institucional Universidad Nacional de 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Development of a software method to assist in the thematic analysis of responses to open ended questions in Spanish-language surveys

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