Introduction
Welcome to the thematization module documentation for the R3M Score platform. This toolkit introduces the new AI-assisted coding feature for open questions (OQ), designed to streamline and enhance the qualitative analysis of verbatim responses.
What is thematization?
Thematization is the process of coding open-ended responses (verbatims) into organized themes and ideas. By structuring qualitative data, you can extract actionable insights, quantify recurring topics, and make your research more robust and comparable.
AI-assisted coding
With the R3M Score platform, you can now leverage artificial intelligence to:
- Automatically generate a code frame from your verbatim files or import an existing code plan.
- Efficiently code and thematize verbatims, saving time and reducing manual effort.
- Retrieve coded files ready for further analysis and integration with quantitative data.
How it works
The thematization workflow consists of two main steps:
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Creation or integration of a code frame: Build a new code frame from your verbatim data, or upload an existing coding plan to use as a reference.
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Codification of verbatims: Apply the code frame to your open-ended responses. The platform will process, code, and return a structured file for analysis.
Each project is linked to a single code frame. If you want to code different types of responses (e.g., Likes and Dislikes), create a separate project for each.
Why use thematization?
- Faster and more consistent coding with AI assistance.
- Easier extraction of actionable insights from qualitative data.
- Seamless integration with quantitative analysis and reporting.
This documentation will guide you through every stage of the thematization process, from project setup and code frame management to coding, output interpretation, and best practices.
If you need support or have questions, please refer to the support resources at the end of this documentation.