fsqca-analysis
CommunityTurn fsQCA research into clear causal configurations.
System Documentation
What problem does it solve?
This skill helps you conduct fuzzy-set Qualitative Comparative Analysis (fsQCA) end-to-end by combining qualitative theory work with quantitative calibration, truth-table construction, and logic minimization.
Core Features & Use Cases
- Four-phase fsQCA workflow: theoretical analysis → calibration guidance → quantitative computation → result interpretation.
- Fuzzy calibration methods: supports multiple calibration approaches (direct, threshold, interpolation, gaussian, sigmoid, indirect) to convert raw data into 0–1 membership scores.
- Truth table + minimization: builds a fuzzy truth table, flags contradictions, handles remainders, and produces complex/primed/intermediate solution candidates.
- Research-ready outputs: generates structured report sections and interpretation guidance to explain causal mechanisms (configurations) in theory language.
Use case example: You have a set of cases with continuous indicators for conditions (A, B, C) and an outcome (Y); you want to identify multiple causal paths (configurations) that consistently explain high Y across the case set.
Quick Start
Run the fsqca-analysis skill by executing: cd fsqca-analysis/ then python scripts/integrated_analysis.py.
Dependency Matrix
Required Modules
Components
💻 Claude Code Installation
Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.
Please help me install this Skill: Name: fsqca-analysis Download link: https://github.com/ptreezh/sscisubagent-skills/archive/main.zip#fsqca-analysis Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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