grounded-theory-expert

To run grounded theory analysis, provide your Chinese interview text as structured JSON with text_data or segments. The skill executes open coding to extract initial concepts, axial coding to build paradigm models, and selective coding to form a coherent storyline with propositions.

Theory saturation is checked by feeding new_data into the skill to determine whether it yields new concepts or relationships. The skill evaluates this additional qualitative data against existing categories to support saturation decisions, indicating when theoretical sampling should stop.

Jieba and pandas support open coding on qualitative observation documents by handling Chinese text segmentation and structured data manipulation. The skill leverages these dependencies to extract initial concepts from raw text and organize them with definitions and examples.

Building a paradigm model from axial coding results involves clustering initial concepts into categories and analyzing their properties. The skill constructs a simplified paradigm model mapping conditions, context, actions, and consequences from your qualitative data.

This grounded theory tool works with planning workflows through optional integration. It satisfies deterministic multi-stage analysis requirements by providing structured JSON outputs and allowing coding_stage control to fit into broader research pipelines.

Automated memo writing in qualitative research generates stage-appropriate memos based on coding outputs but lacks the researcher's implicit contextual intuition. The skill provides structured memo generation tied to open, axial, and selective coding stages to support rather than replace analytical judgment.