Dimensions and clusters: A hybrid approach to classification
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Dimensions and clusters: A hybrid approach to classification
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1979
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Abstract
A hybrid strategy is described for integrating the
dimensional and discrete clusters approaches to
classification research. First, a parsimonious set of
dimensions is sought through a multiple replications
design. The computations employ a two-stage
least squares solution that is based on a sequential
application of the Eckart and Young
(1936) decomposition. Second, relatively homogeneous
subgroups are identified within this low
dimensional space using a clustering or density
search algorithm. To facilitate interpretation of the
final solution, an ideal type concept is introduced
that is similar to the "idealized individual" interpretation
of multidimensional scaling. Depending
upon the model chosen, the independent contribution
of elevation, scatter, and shape parameters
may be differentiated in defining profile similarity.
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Skinner, Harvey A. (1979). Dimensions and clusters: A hybrid approach to classification. Applied Psychological Measurement, 3, 327-341. doi:10.1177/014662167900300305
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doi:10.1177/014662167900300305
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Skinner, Harvey A.. (1979). Dimensions and clusters: A hybrid approach to classification. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/99632.
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