Hierarchical Vs. Generally Overlapping Models In Psychiatric Classification

This paper illustrates two formal models for psychiatric classification. The first model, called a hierarchical or tree structure, requires patient categories to be disjoint or strictly nested. The second model, called the generally overlapping or network model, allows patient categories to cut across each other in a variety of different ways. Thus, patient groups can be disjoint, strictly nested (as in a hierarchy), or partially overlapping. To derive classification schemes consistent with the structural models, two different clustering techniques were applied to interpatient similarity data collected on 50 psychiatric patients. A hierarchical clustering technique was applied to the similarity data to obtain a hierarchical classification. To obtain a generally overlapping classification, Peay's cliquing procedure was applied to the same data. Two criteria were used to compare the clustering solutions. First, a solution's goodness-of-fit to the original data was examined by calculating the proportion of variance accounted for by cluster categories. Second, the predictive accuracy of a solution was analyzed by looking at the categories' ability to predict treatment assignment. The generally overlapping solution produced the best fit to the original similarity data; however, the hierarchical solution's clusters tended to be more readily interpretable in terms of psychiatric syndromes. Both clustering solutions were relatively poor predictors of treatment assignment. It was concluded that the hierarchical and generally overlapping approaches, although not conclusively demonstrated, represented promising models for psychiatric classification.