The Bootstrapped Robustness Assessment for Qualitative Comparative Analysis

• Main Authors: Gibson, C. Ben, VannJr, Burrel
• Format: Journal Article
• Language: English
• Published: 15-06-2016
• Subjects: Statistics - Methodology
• DOI: 10.48550/arxiv.1606.05000

Qualitative Comparative Analysis (QCA) has been increasingly used in recent
years due to its purported construction of a middle path between case-oriented
and variable-oriented methods. Despite its popularity, a key element of the
method has been criticized for possibly not distinguishing random from real
patterns in data, rendering its usefulness questionable. Critics of the method
suggest a straightforward technique to test whether QCA will return a
configuration when given random data. We adapt this technique to determine the
probability that a given QCA application would return a random result. This
assessment can be used as a hypothesis test for QCA, with an interpretation
similar to a p-value. Using repeated applications of QCA to randomly-generated
data, we first show that generally, the tendency for QCA to return spurious
results is attenuated by using reasonable consistency score and configurational
N thresholds; however, this varies considerably according to the basic
structure of the data. Second, we suggest an application-specific assessment of
QCA results, illustrated using the case of Tea Party rallies in Florida. This
method, which we coin the Bootstrapped Robustness Assessment for QCA (baQCA),
can provide researchers with recommendations for consistency score and
configurational N thresholds.