Introduction

Abstract

Psychology has long been concerned with the problem of grouping and classification, and this concern is also an area of interest for neuropsychology, where numerous classificatory systems have been described. For example, there are systems for classifying various types of aphasia, systems for grouping dementias and amnestic disorders, and systems for classification of developmental disorders. Researchers and clinicians have applied a number of different methods to develop these systems, but with the publication of Sokal and Sneath's book Principles of Numerical Taxonomy in 1963, numerical statistical approaches such as cluster analysis were presented as alternatives to subjective approaches to classification. Since then, neuropsychological investigations have applied cluster analysis primarily in the areas of learning disability, traumatic brain injury, and schizophrenia. Given the increasing application of cluster analysis to neuropsychological variables, this volume provides an overview of the statistical underpinnings of cluster analysis, including discussion of some of the most common challenges that face investigators who apply cluster analysis in their work. Chapters are also included that present various applications of cluster analysis to the classification of such disorders as schizophrenia and traumatic brain injury, as well as the study of memory development in normally developing children and adolescents. These chapters contain a step-by-step approach with the intent of providing the reader with a clear understanding of the application of cluster analysis from initial design to implementation and interpretation of results. The final chapter contains concluding remarks with suggestions for future applications of cluster analysis to studies of cognitive function in clinical and normal populations.