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Qualitative analysis is the process of identifying elements, ions, or compounds in an unknown sample. It is the first and most fundamental type of analysis based on the hierarchy of analytical goals. This hierarchy is significant as it provides a structured approach to scientific research, with qualitative analysis serving as the initial step, providing essential information before moving on to quantitative or other forms of analysis.
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Project-Based Learning Guidelines for Health Sciences Students: An Analysis with Data Mining and Qualitative Techniques
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Developing and testing an automated qualitative assistant (AQUA) to support qualitative analysis.

Robert P Lennon1, Robbie Fraleigh2, Lauren J Van Scoy3

  • 1Family and Community Medicine, Penn State Health Milton S. Hershey Medical Center, Hershey, Pennsylvania, USA rlennon@pennstatehealth.psu.edu.

Family Medicine and Community Health
|November 26, 2021
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Summary

Automated qualitative assistant (AQUA) uses transparent AI methods to code qualitative data, reducing time and cost. AQUA achieves human-level reliability for automated coding, enhancing research efficiency and reproducibility.

Keywords:
qualitative research

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Area of Science:

  • Artificial Intelligence in Qualitative Research
  • Computational Social Science
  • Health Informatics

Background:

  • Qualitative data analysis (coding) is time-consuming and costly, limiting research.
  • Existing AI methods for qualitative coding often lack transparency, hindering adoption.
  • Need for transparent and reproducible AI tools in qualitative research.

Purpose of the Study:

  • Introduce Automated Qualitative Assistant (AQUA), a transparent AI tool for qualitative data analysis.
  • Demonstrate AQUA's ability to generate unsupervised topic categories and hierarchical representations.
  • Validate AQUA's performance in automated coding against human intercoder reliability.

Main Methods:

  • Developed AQUA using a graph-theoretic approach for transparent topic extraction and clustering.
  • Applied AQUA to a large dataset of free-text survey responses.
  • Evaluated AQUA's coding performance using Cohen's kappa for intercoder reliability.

Main Results:

  • AQUA generated unsupervised topic categories and hierarchical data representations.
  • AQUA achieved Cohen's kappa values of 0.62-0.72, comparable to human coders for specific categories.
  • Demonstrated potential for automating coding on large datasets.

Conclusions:

  • AQUA offers a transparent and reproducible AI-assisted method for qualitative data coding.
  • Primary care researchers can use AQUA to efficiently code large text datasets.
  • This work promotes best practices for AI/ML-assisted qualitative research.