scpQCA: Enhancing mvQCA Applications through Set-Covering-Based QCA Method

TL;DR

This paper introduces scpQCA, an improved QCA method that enhances coverage and multi-factor analysis capabilities, validated through experiments on various datasets showing its robustness and broader applicability.

Contribution

The paper presents scpQCA, a novel set-covering-based QCA method that overcomes coverage and factor limitations of traditional QCA techniques.

Findings

scpQCA improves coverage in QCA applications

It expands tolerance for multi-factor and multi-valued analyses

Demonstrates robustness across diverse datasets

Abstract

In fields such as sociology, political science, public administration, and business management, particularly in the direction of international relations, Qualitative Comparative Analysis (QCA) has been widely adopted as a research method. This article addresses the limitations of the QCA method in its application, specifically in terms of low coverage, factor limitations, and value limitations. scpQCA enhances the coverage of results and expands the tolerance of the QCA method for multi-factor and multi-valued analyses by maintaining the consistency threshold. To validate these capabilities, we conducted experiments on both random data and specific case datasets, utilizing different approaches of CCM (Configurational Comparative Methods) such as scpQCA, CNA, and QCApro, and presented the different results. In addition, the robustness of scpQCA has been examined from the perspectives of internal and external across different case datasets, thereby demonstrating its extensive applicability and advantages over existing QCA algorithms.