Seven association measures were compared for
their effectiveness in relating and clustering data
profiles confounded with interrater differences.
Among these association indices were three distance
measures, two measures of angular separation, and
two measures of profile overlap. The objects of
analysis were 50 jobs that had been rated by different
analysts on the items comprising the Occupation
Analysis Inventory (OAI). Factor scores based
on the OAI job ratings provided the profile data.
Each of the seven association measures was applied
to all pairwise combinations of factor-score profiles
in the job sample, and the seven resultant 50 x 50
job proximity matrices were each subjected to hierarchical
cluster analysis. The job proximity matrices
and cluster structures based on the different
association measures were then compared, respectively,
with a criterion proximity matrix and a
criterion cluster structure. In relation to these two
criteria, the angular measures (product-moment
correlation and cosine) performed better than the
distance and overlap measures. The results demonstrate
the importance of the choice of a profile association
measure in cluster analysis. The researcher
should be especially cautious when clustering entities
that have been rated by different judges. Under
such circumstances, it might be advisable to
cluster analyze a data set using more than one association
measure and then to compare the alternative
solutions for clarity and stability.
Hamer, Robert M & Cunningham, J. W. (1981). Cluster analyzing profile data confounded with interrater differences: A comparison of profile association measures. Applied Psychological Measurement, 5, 63-72. doi:10.1177/014662168100500109
Hamer, Robert M.; Cunningham, J. W..
Cluster analyzing profile data confounded with interrater differences: A comparison of profile association measures.
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