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msqca-analysis

Perform multi-value Qualitative Comparative Analysis with boolean minimization and truth-table construction.

24|7|Updated Nov 15, 2025
Search Tags:#report generation#calibration#research workflow#truth table#causal mechanisms#msqca#boolean minimization
One-click install
npx skills add https://github.com/ptreezh/sscisubagent-skills --skill msqca-analysis
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: msqca-analysis
Source: https://github.com/ptreezh/sscisubagent-skills/tree/main/skills/msqca-analysis
Command: npx skills add https://github.com/ptreezh/sscisubagent-skills --skill msqca-analysis

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, numpy, scipy, scikit-learn, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

msQCA analysis coordinates qualitative theory building with data calibration and boolean minimization to derive causal condition configurations for complex cases.

Core Features & Use Cases

  • Four-phase integrated workflow: theoretical analysis → calibration guidance → quantitative msQCA computation → result interpretation.
  • Calibration planning guidance: generates per-variable calibration decisions by combining theoretical anchors with data characteristics and quality checks.
  • Truth table + minimization engine: builds a truth table, flags contradiction configurations, and produces multiple solution types (complex/minimal/parsimonious).
  • Report-oriented outputs: produces an integrated report structure and quality metrics to support transparent interpretation.

Quick Start

Ask the AI to run an msQCA integrated analysis by providing your dataset (CSV) with case_id, condition variables, and a calibrated outcome variable Y, then request an interpretation of the best solution and its causal mechanisms.

Frequently Asked Questions about msqca-analysis

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I run multi-value QCA with theory-guided calibration for social science research?▼

Multi-value QCA with theory-guided calibration is executed through a four-phase workflow: theoretical analysis, calibration guidance, quantitative computation, and interpretation. You provide a CSV dataset containing case identifiers and condition variables to derive causal configurations.

How does boolean minimization handle contradiction configurations when building a truth table?▼

Boolean minimization handles contradiction configurations by flagging them during truth-table construction. The engine then processes these flagged configurations to produce complex, minimal, and parsimonious solution expressions for interpreting causal mechanisms.

What is the best way to calibrate multi-category conditions for configurational causal modeling?▼

Calibrating multi-category conditions requires combining theoretical anchors with data characteristics. The process generates per-variable calibration decisions and applies quality checks to ensure accurate truth-table construction for configurational causal modeling across multiple cases.

Do I need a CSV dataset with specific variables to perform msQCA analysis?▼

Yes, you need a CSV dataset containing a case_id column, condition variables, and a calibrated outcome variable Y. This structured input drives the truth-table construction and boolean minimization to identify causal condition configurations.

Can pandas and scikit-learn be used for qualitative comparative analysis of multiple cases?▼

Pandas and scikit-learn support the underlying computational scripts for qualitative comparative analysis. These dependencies process the calibrated data, build truth tables, and execute boolean minimization to output optimized solution expressions.

Why does msQCA require theory-driven condition selection before quantitative computation?▼

Theory-driven condition selection establishes the qualitative framework for multi-value QCA. It ensures the calibrated conditions align with social science research scenarios before boolean minimization computes the optimized causal solutions.

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