A New Development to Aid Interpretation of Hierarchical Cluster Analysis of Repertory Grid Data

Hierarchical clustering is a common way to analyze repertory grid data, as it allows visual identification of homogeneous groups of elements and constructs. This article explores a bootstrap approach to clustering that additionally allows significance testing of dendrogram structures. Given that the choice of the specific clustering method and distance measure can be justified, the method yields indications of closely related element or construct structures. Closely integrated groups of constructs link to the concept of cognitive complexity. Several new complexity indexes are derived from the bootstrap solution. The behavior of the indexes is compared (a) across the most common clustering methods and distance measures and (b) to standard complexity measures. The derived measures reflect a different aspect of complexity than standard measures.