Four methods of cluster analysis were examined
for their accuracy in clustering simulated job
analytic data. The methods included hierarchical
mode analysis, Ward’s method, k-means method
from a random start, and k-means based on the results
of Ward’s method. Thirty data sets, which differed
according to number of jobs, number of
population clusters, number of job dimensions, degree
of cluster separation, and size of population
clusters, were generated using a monte carlo technique.
The results from each of the four methods
were then compared to actual classifications. The
performance of hierarchical mode analysis was significantly
poorer than that of the other three
methods. Correlations were computed to determine
the effects of the five data set variables on the accuracy
of each method. From an applied perspective,
these relationships indicate which method is
most appropriate for a given data set. These results
are discussed in the context of certain limitations of
this investigation. Suggestions are also made regarding
future directions for cluster analysis research.
Zimmerman, Ray, Jacobs, Rick & Farr, James. (1982). A comparison of the accuracy of four methods for clustering jobs. Applied Psychological Measurement, 6, 353-366. doi:10.1177/014662168200600311
Zimmerman, Ray; Jacobs, Rick; Farr, James L..
A comparison of the accuracy of four methods for clustering jobs.
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