| Summary: | Classification and subtyping have been an important part of psychology from its earliest days, essential to personality, pathology, and other key aspects of the field. Within neuropsychology specifically, improvements in classification have led to greater accuracy in assessment, diagnosis, and approaches to treatment, with cluster analysis recently emerging as a complement or an alternative to clinical observation and to other statistics-based methods. Cluster Analysis in Neuropsychological Research reviews the basics of cluster analysis, examines challenges to its use, and applies its methods to ongoing research questions across neuropsychology. Chapters illustrate the mathematical concepts underlying cluster analysis and overview areas of its most notable use (e.g., learning disabilities, brain injury, schizophrenia) before moving on to current lines of inquiry. Subjects in these studies include individuals with medical pathologies, neurologically stable individuals with diagnosed psychiatric illness, and normal individuals, resulting not only in useful empirical data, but also robust methods by which new studies may be developed. Included in the coverage: Statistical and methodological considerations when using cluster analysis. Application of cluster analysis to investigate neuropsychological heterogeneity in psychiatric and neurological patients. Identifying neurodevelopmental stages of memory from childhood through adolescence with cluster analysis. A neuropsychological approach to classifying TBI severity in youth. Classification with cluster analysis of health problems of homeless veterans. Possibilities for using cluster analysis in other diverse areas of neuropsychology. Neuropsychologists and cognitive neuroscientists will find Cluster Analysis in Neuropsychological Research a stimulating guide to a promising platform for future developments in the field. |
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