Retail Image Dimensions: An Empirical Analysis Page: 58
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attempt to determine whether there were any confounding
variations in the responses.
Multidimensional Scaling of the Data
The next phase was to scale the three data subsets using
the MDSCAL-5M multidimensional scaling program. The three
data subsets are the direct similarities data, the
preferences data and the attribute rankings data.
Multidimensional scaling of the similarities data was
used to create the primary perceptual map of the shopping
center stimuli. The respondent task in creating these data
was to rank each shopping center against the others. All
possible combinations were ranked.
This input matrix was scaled using the MDSCAL-5M program
"a versatile nonmetric multidimensional scaling program
designed for the analysis of similarities" (OSIRIS III 1976,
p. 615) . This program, which operates on a matrix of
similarity (or dissimilarity) measures, is designed to find,
for each specified dimensionality, the best geometric
representation of the data in multidimensional space. The
program allows the user to control the number of iterations
in the scaling algorithm, the number of dimensions the
configuration should be scaled in, the stress level for
program termination, and the handling of ties in the input
coefficients. For each dimensionality, the final
configuration is output with a complete history of the
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Palmer, Arthur M. (Arthur Mohler). Retail Image Dimensions: An Empirical Analysis, dissertation, August 1982; Denton, Texas. (https://digital.library.unt.edu/ark:/67531/metadc330964/m1/67/: accessed April 17, 2024), University of North Texas Libraries, UNT Digital Library, https://digital.library.unt.edu; .