This course is designed to equip students with essential knowledge and skills for conducting qualitative research using Computer-Assisted Qualitative Data Analysis Software (CAQDAS). Students will explore essential concepts and methodologies critical to qualitative research design and implementation, including the theoretical foundations of grounded theory and thematic analysis, as well as practical workflows using CAQDAS tools. The course emphasizes hands-on experience with leading software such as QDAMiner, ATLAS.ti, and yEd, enabling students to efficiently code, organize, and interpret qualitative data. The course will emphasize utilizing CAQDAS tools for systematic categorization and linking of codes, applying network analysis for visual representation, and refining thematic identification and interpretation.
A significant component of the course will focus on the integration of Artificial Intelligence (AI) in qualitative data analysis. Students will explore how AI, including Natural Language Processing (NLP) and Large Language Models (LLMs), can assist in coding and thematic analysis. The course will also address the reliability and ethical considerations of AI-assisted qualitative research.
By successfully completing this course, students will be well-prepared to apply qualitative-driven research methods in their future professional careers, contributing meaningfully to their chosen disciplines.
This course is designed to equip students with essential knowledge and skills for conducting qualitative research using Computer-Assisted Qualitative Data Analysis Software (CAQDAS). Students will explore key concepts and methodologies critical to qualitative research design and implementation, including the theoretical foundations of grounded theory approach and thematic analysis and the and practical workflows by using CAQDAS tools. The course emphasizes hands-on experience with leading software such as QDAMiner, ATLAS.ti, and yEd, enabling students to efficiently code, organize, and interpret qualitative data.
Throughout the course, students will gain a comprehensive understanding of the principles and workflows of computer-assisted qualitative data analysis. This includes exploring the ontological and epistemological considerations that inform the use of CAQDAS, understanding the various stages of qualitative data analysis, and grasping the fundamental functionalities that are common across different CAQDAS packages. Furthermore, the course will focus on how to utilize software features for systematic categorization and linking of codes, incorporating techniques such as selective coding and constant comparison (in grounded theory), The application of network analysis tools within CAQDAS will also be introduced to visually represent relationships between codes and concepts, ultimately facilitating deeper levels of data interpretation. In addition to that, thematic identification, refinement, and interpretation (in thematic analysis) will be also discussed.
A significant component of the course will focus on the integration of Artificial Intelligence (AI) in qualitative data analysis. Students will explore how AI, including Natural Language Processing (NLP) and Large Language Models (LLMs), can assist in coding and thematic analysis. The course will also address the reliability and ethical considerations of AI-assisted qualitative research, providing students with
Capability (skills)
Qualitative research design and implementation: Students will be able to design, implement, and evaluate qualitative research projects, applying CAQDAS tools to enhance the rigor and transparency of their analyses.
Data analysis using CAQDAS: Students will develop proficiency in using leading qualitative analysis software such as QDAMiner, ATLAS.ti, and yEd, enhancing their technical skills in qualitative research and fostering adaptability to new technologies.
Systematic categorization and linking of codes:Students will gain practical skills in utilizing CAQDAS features for systematic categorization and linking of codes.
Network analysis and thematic interpretation: Students will learn to apply network analysis tools within CAQDAS to visually represent relationships between codes and concepts, facilitating deeper levels of data interpretation and thematic identification, refinement, and interpretation.
AI integration in qualitative analysis: Students will gain practical skills in integrating AI, including NLP and LLMs, into qualitative data analysis, and critically evaluating the reliability and ethical considerations of AI-assisted research.
Knowledge
Qualitative research methods: Students will gain in-depth knowledge of qualitative research procedures, including data collection, coding, and thematic analysis, supported by CAQDAS tools. They will explore the theoretical foundations and practical applications of grounded theory and thematic analysis, learning how to systematically develop and refine codes, identify themes, and interpret qualitative data. This comprehensive understanding will enable students to apply these methodologies effectively in their research projects.
AI in qualitative research: Students will learn about the potential and challenges of integrating AI in qualitative data analysis, including methods for evaluating the reliability and consistency of AI-assisted coding.
Attitude
Ethical research conduct: Students will develop
There are no specific prerequisites for this course, as it begins with foundational concepts and gradually introduces key qualitative research methods. It is designed for learners at all levels, including those with no prior experience in empirical research
Resources
Babbie, E. R. (2014). The practice of social research. Cengage Learning.
Bryman, A. (2016). Social research methods. Oxford University Press.
May, T., & Perry, B. (2022). Social research: Issues, methods and process. McGraw-Hill Education.
Neuman, W. L. (2014). Social research methods: Qualitative and quantitative approaches. Pearson Education Limited.
Emphasizing student-centred learning, transversal skills, transdisciplinary, research-based competencies, and technology-enhanced educational principles, this course is designed to provide students with a comprehensive and practical understanding of qualitative research methods using CAQDAS tools.
Students will engage in interactive lectures, discussions, and group activities that encourage active participation and critical thinking. Regular feedback sessions will be conducted to address individual learning needs and progress, ensuring that each student receives personalized guidance. Additionally, a peer review system will be implemented where students evaluate each other’s work from different disciplinary perspectives.
To improve students’ transversal skills, the course will develop their ability to present research and analyse findings clearly and effectively, both in written and oral formats by incorporating regular presentations, peer reviews, and feedback sessions. Emphasis will be placed on developing skills to critically evaluate research methodologies and data by engaging students in rigorous analysis exercises, case studies, and interactive discussions that challenge their analytical thinking and problem-solving abilities.
To further develop students’ research skills, the course will focus on an in-depth exploration of key concepts and methodologies critical to qualitative research design and implementation. This includes hands-on experience with qualitative research procedures, such as coding, thematic analysis, and the use of CAQDAS tools like QDAMiner, ATLAS.ti, and yEd. Students will also explore the integration of AI in qualitative data analysis, gaining practical skills in using NLP and LLMs for coding and thematic analysis.
To enrich technology -enhanced learning experiences, digital tools and resources will be integrated, including online databases, qualitative analysis software, and virtual collaboration platforms.
Transcript of records
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