Cluster analysis in community research: Epistemology and practice |
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Authors: | Bruce D. Rapkin Douglas A. Luke |
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Affiliation: | (1) Memorial Sloan-Kettering Cancer Center, 1275 York Avenue, Box 570, 10021 New York, New York;(2) Michigan State University, Michigan, USA |
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Abstract: | Cluster analysis refers to a family of methods for identifying cases with distinctive characteristics in heterogeneous samples and combining them into homogeneous groups. This approach provides a great deal of information about the types of cases and the distributions of variables in a sample. This paper considers cluster analysis as a quantitative complement to the traditional linear statistics that often characterize community psychology research. Cluster analysis emphasizes diversity rather than central tendency. This makes it a valuable tool for a wide range of familiar problems in community research. A number of these applications are considered here, including the assessment of change over time, network composition, network density, person-setting relationships, and community diversity. A User's Guide section is included, which outlines the major decisions involved in a basic cluster analyses. Despite difficulties associated with the identification of optimal cluster solutions, carefully planned, theoretically informed application of cluster analysis has much to offer community researchers.Editor's note: Dr. Edward Seidman served as action editor for this article while serving as Associate Editor for Methodology. |
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Keywords: | cluster analysis community diversity heterogeneous samples social networks |
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