A single higher-order cluster analysis can be
used to group cluster mean profiles derived from
several preliminary analyses. Replication is confirmed
when each higher-order cluster contains one cluster
mean profile from each of the several preliminary
analyses. This study evaluated the utility of
replication as a stopping rule in hierarchical cluster
analysis. Replication defined by higher-order
clustering identifies the correct number of underlying
populations that have distinct density regions
in the multivariate measurement space. When
increased within-population variance obliterates
population distinctions, the replication criterion
provides an underestimation of the actual number
of latent populations. In the case of no true
cluster structure or in the case of only two latent
populations, chance replication can occur. Thus,
replication suggested by higher-order cluster
analysis is not a conservative test for the absence
of a cluster structure, but it does provide valid
evidence concerning the number of latent populations
when several are present. Index terms:
cluster analysis, cluster means, hierarchical clustering,
replication in cluster analysis, stopping rule
in cluster analysis, validity of cluster analysis.
Overall, John E & Magee, Kevin N. (1992). Replication as a rule for determining the number of clusters in hierarchical cluster analysis. Applied Psychological Measurement, 16, 119-128. doi:10.1177/014662169201600202
Overall, John E.; Magee, Kevin N..
Replication as a rule for determining the number of clusters in hierarchical cluster analysis.
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