Table of Contents

A Larger-N, Fewer Variables Problem? The Counterintuitive Sensitivity of QCA

Chris Krogslund, University of California, Berkeley
Katherine Michel, University of California, Berkeley

QCA is of Questionable Value for Policy Research

Sean Tanner, University of California, Berkeley

QCA and Causal Inference: A Poor Match for Public Policy Research

Sean Tanner, University of California, Berkeley


POLITICAL METHODS: COMPUTATIONAL eJOURNAL

"A Larger-N, Fewer Variables Problem? The Counterintuitive Sensitivity of QCA" Free Download
Qualitative & Multi-Method Research, Spring 2014, Vol. 12, No. 1

CHRIS KROGSLUND, University of California, Berkeley
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KATHERINE MICHEL, University of California, Berkeley
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Studies employing Qualitative Comparative Analysis (QCA) often analyze a relatively small number of cases (n) to assess the impact of a substantial number of variables (k) on a given outcome. In the tradition of writing on the comparative method and multi-method research, however, this ratio of cases-to-variables is viewed as an analytic problem. In this exploratory research note, we raise questions about the implications of this ratio for the stability of findings in QCA. One common method of assessing result stability is the “drop-one? sensitivity test, which repeatedly reruns a particular analysis, each time dropping a single case. We find that, for the number of cases to which analysts most routinely apply QCA, this type of sensitivity analysis produces paradoxical results. Directly contrary to the standard expectation that more robust findings emerge with a higher n/k ratio, we discover that QCA findings based on a lower n/k ratio prove to be more stable. This result calls into question the validity of the drop-one test as a sensitivity metric for QCA.

"QCA is of Questionable Value for Policy Research" Free Download
Policy and Society 33 (2014) 287–298

SEAN TANNER, University of California, Berkeley
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Qualitative Comparative Analysis (QCA) has been championed as a valuable tool for public policy research. Focusing on the field of policy evaluation, this research note assesses QCA by comparing research that uses this method to studies based on standard practices for quantitative policy analysis. While attention is centrally focused on causal inference, questions of measurement are also addressed. The analysis suggests that QCA adds little value to current methods of policy scholarship, and its contribution in fact falls far short, compared with present-day standard practices. For example, a properly defined "net effects" framework – which is pointedly rejected by QCA – provides valuable insights regarding the causal effects that are a central concern of policy evaluation. By contrast, as an approach to policy analysis, QCA suffers from severe limitations in both its framework and its findings.

"QCA and Causal Inference: A Poor Match for Public Policy Research" Free Download
Qualitative & Multi-Method Research, 12(1), 15-24

SEAN TANNER, University of California, Berkeley
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Qualitative Comparative Analysis (QCA) offers distinctive research tools that, according to its practitioners, yield a productive solution to many problems and limitations of conventional quantitative methods. This article analyzes the contributions of QCA to policy evaluation vis-a-vis standard means for obtaining for causal inference and finds the method lacking.

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