Applying cluster analysis in counseling psychology research.

As a research technique that has grown rapidly in applications in many scientific disciplines, cluster analysis has potential for wider use in counseling psychology research. We begin with a simple example illustrating the clustering approach. Topics covered include the variety of approaches in clustering, the times when cluster analysis may be a choice for analysis, the steps in cluster analysis, the data features, such as level, shape, and scatter, that affect cluster results, alternate clustering methods and evidence indicating which are most effective, and examples of clustering applications in counseling research. Although we make an attempt to provide a comprehensive overview of major issues, the reader is encouraged to consult several good recent publications on the topic that are especially relevant for psychologists. Cluster analysis is a classification technique for forming homogeneous groups within complex data sets. Both the clustering methods and the ways of applying them are extremely diverse. Our purpose in writing this article is to provide an introduction and a road map for applying these techniques productively to research in counseling psychology. The cluster analysis literature is huge, is scattered among many diverse disciplines, and is often arcane. We have made an attempt to cull those aspects most relevant and useful to psychologists from this literature. Most of the discussion in the psychological community about how best to apply cluster analysis to obtain robust, valid, and useful results has taken place within the past 5 years. We seem to be on the verge of a consensus, which has long been needed in an often bewildering field. In the past 30 years, a number of clustering methods, often with their own vocabulary and approaches, have sprouted within a wide variety of scientific disciplines. The earliest sustained applications were in problems of biological classification, within the field called numerical taxonomy (Sokal & Sneath, 1963). Today, clustering is applied to problems as different as the grouping of chemical structures (Massart & Kaufman, 1983) and the classification of helpful and nonhelpful events in counseling (Elliott, 1985). Computerized methods for generating clusters have been developed and made increasingly available over the last decade. Applications of clustering have mushroomed in many disciplines, including the social sciences. In an annual bibliographic search performed by the Classification Society (Day, 1986) 1,166 entries are shown for the 1985 scientific literature alone.