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Two-sample test for ambivalent subset relationship in fuzzy set qualitative comparative analysis


Veri, Francesco (2024). Two-sample test for ambivalent subset relationship in fuzzy set qualitative comparative analysis. Quality & Quantity, 58(2):1235-1253.

Abstract

In fuzzy set qualitative comparative analysis (fsQCA), ambivalent subset relationships (ASR), occur when solution term X is in subset relation with the outcome Y and its absence ~ Y, leading to false-positive results. While ASR can be empirically detected in small-N and medium-N cases through in-depth case knowledge, it is challenging to identify them in large-N case designs. QCA parameters such as proportion reduction inconsistency (PRI) and consistency are commonly used to identify simultaneous subset relationships (SSR), but they are not specifically designed to detect ASR. To address this issue, this article introduces the DTS test, a new test based on two-sample statistics. The DTS test identifies distributional convergence between a solution term’s empirical cumulative distribution function (eCDF) and an eCDF of solution formulas with asymptotic ASR characteristics. By comparing empirical solutions’ patterns with spurious artificially built solutions' patterns, the DTS test reduces the risk of causal fallacies in interpreting the empirical results. Overall, the DTS test provides a valuable tool for identifying and addressing potential ASR bias in fsQCA, particularly in large-N case designs.

Abstract

In fuzzy set qualitative comparative analysis (fsQCA), ambivalent subset relationships (ASR), occur when solution term X is in subset relation with the outcome Y and its absence ~ Y, leading to false-positive results. While ASR can be empirically detected in small-N and medium-N cases through in-depth case knowledge, it is challenging to identify them in large-N case designs. QCA parameters such as proportion reduction inconsistency (PRI) and consistency are commonly used to identify simultaneous subset relationships (SSR), but they are not specifically designed to detect ASR. To address this issue, this article introduces the DTS test, a new test based on two-sample statistics. The DTS test identifies distributional convergence between a solution term’s empirical cumulative distribution function (eCDF) and an eCDF of solution formulas with asymptotic ASR characteristics. By comparing empirical solutions’ patterns with spurious artificially built solutions' patterns, the DTS test reduces the risk of causal fallacies in interpreting the empirical results. Overall, the DTS test provides a valuable tool for identifying and addressing potential ASR bias in fsQCA, particularly in large-N case designs.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:06 Faculty of Arts > Institute of Political Science
Dewey Decimal Classification:320 Political science
Scopus Subject Areas:Physical Sciences > Statistics and Probability
Social Sciences & Humanities > General Social Sciences
Uncontrolled Keywords:False positive Qca Ambivalent subsets relationship Two sample test Set theory Parameters of fit Spuriousness
Language:English
Date:1 April 2024
Deposited On:15 Feb 2024 16:44
Last Modified:30 Jun 2024 03:34
Publisher:Springer
ISSN:0033-5177
OA Status:Hybrid
Publisher DOI:https://doi.org/10.1007/s11135-023-01687-8
Project Information:
  • : FunderUniversity of Zurich
  • : Grant ID
  • : Project Title
  • Content: Published Version
  • Language: English
  • Licence: Creative Commons: Attribution 4.0 International (CC BY 4.0)