Cluster analysis in community research: Epistemology and practice

TLDR

Cluster analysis identifies distinct groups within heterogeneous samples, revealing case types and variable distributions while emphasizing diversity over central tendency. This paper considers cluster analysis as a quantitative complement to traditional linear statistics in community psychology research. The authors discuss applications such as assessing change over time, network composition, density, person‑setting relationships, and community diversity, and provide a User’s Guide outlining key decisions in basic cluster analyses. Cluster analysis proves valuable for a wide range of community research problems.

Abstract

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.