Clustered set-relational data in Qualitative Comparative Analysis (QCA) can have a hierarchical structure, a panel structure or repeated cross sections. 'QCAcluster' allows researchers to supplement the analysis of pooled the data with a differentiated perspective focusing on selected partitions of the data. The pooled data can be partitioned along the dimensions of the clustered data (individual cross sections or time series) to perform partition-specific truth table minimization. Empirical researchers can further calculate the weight that each partition has on the parameters of the pooled solution and the diversity of the cases under analysis within and across partitions (see < https://ingorohlfing.github.io/QCAcluster/>).
QCACluster
Three people have contributed to QCAcluster. In alphabetical order:
Clustered data can take different forms in empirical research. The data
might have a hierarchical structure (lower-level units nested in
higher-level units); we might have multiple units nested in time (panel
data); or the combination of both. The R package QCAcluster includes
multiple tools for the analysis of clustered data in Qualitative
Comparative Analysis. The use of the tools promises insights that would
go unnoticed in a pooled analysis ignoring the clusters in the data.
Version 0.2.0 of the package is on Github. This version is not on CRAN yet, but should be soon.
pak::pak("ingorohlfing/QCAcluster")
Work on the package was funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant agreement number 638425, Enhanced Qualitative and Multimethod Research).