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Cluster analysis in community research: Epistemology and practice
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.
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Metadata
- Authors
- Rapkin, B. D., & Luke, D. A.
- Venue
- American Journal of Community Psychology
- Publisher
- Wiley
- Published
- 1993-04
- Volume
- 21
- Issue
- 2
- Pages
- 247-277
- ISSN
- 0091-0562 (ISSN-L)
- 1573-2770
- OpenAlex
- W1989894849
- Cited by
- 258
- References
- 39
- FWCI
- 6.42
- Type
- journal article
- Language
- en
- Open access
- closed
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