ClusterCirc: Finding Item Clusters for Circumplex Instruments

Abstract

We introduce ClusterCirc, a new clustering method for circumplex instruments. ClusterCirc finds item clusters with optimal circumplex spacing of items and clusters. In our simulation study, ClusterCirc outperformed Ward and k-means cluster analysis in revealing circumplex clusters, especially for larger within-cluster distances, also when clusters were not equally spaced and for hierarchical models. In empirical data, ClusterCirc yielded subscales with good scale properties and greater circumplex fit than the original subscales and subscales based on Ward cluster analysis. We provide an R package for ClusterCirc (https://github.com/ancleo/ClusterCirc) and SPSS codes (https://github.com/ancleo/ClusterCirc_SPSS) with three functions: ClusterCirc-Data performs ClusterCirc on empirical data. ClusterCirc-Simu performs a tailored simulation study to assess circumplex fit of the data. ClusterCirc-Fix computes ClusterCirc indices for user-defined item clusters.