Case-to-factor Ratios and Model Specification in Qualitative Comparative Analysis.

Area of Science:

• Social Sciences
• Methodology

Background:

• Qualitative comparative analysis (QCA) is a popular empirical research method.
• Concerns exist regarding QCA's susceptibility to causal fallacies with non-causal data, particularly with unfavorable case-to-factor ratios.

Purpose of the Study:

• To challenge the notion that QCA is prone to inferential breakdown due to case-to-factor ratios.
• To demonstrate that established benchmarks for QCA may lead to more fallacious inferences.

Main Methods:

• The study critically examines the methodological underpinnings of qualitative comparative analysis (QCA).
• It analyzes the impact of case-to-factor ratios on inferential validity in QCA.
• The research evaluates the efficacy of existing benchmark tables used to guide QCA application.

Main Results:

• Fears of inferential breakdown in QCA based on case-to-factor ratios are unfounded.
• Benchmark tables, designed to limit exogenous factors, paradoxically increase fallacious inferences.
• Relying on existing field knowledge is more effective for valid causal inference in QCA.

Conclusions:

• The perceived limitations of qualitative comparative analysis (QCA) regarding case-to-factor ratios are not supported.
• Methodological benchmarks in QCA can be counterproductive, leading to flawed causal inferences.
• Researchers should prioritize substantive knowledge over rigid methodological benchmarks for robust QCA findings.