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Abstract
Cluster analysis is an interdependence technique which has been used most successfully with ratio or interval data. After reviewing some of the historical difficulties associated with this method, areas of crucial importance including problem selection, data preparation, process selection and cluster solutions are addressed. Alternative techniques including AID and Q‐factor analysis are featured briefly by way of comparison and a final nine‐point recipe for providing reliable and valid cluster solutions is included. Overall, the paper provides a clear guide to the range of cluster analysis tools available and discusses the potential difficulties involved in their application.
