Proscal5.0 is now available. Changes from Proscal4.5 include the following:
- an ability to evaluate liking ratings under the assumption of
dependent sampling
- an improved initial estimation procedure for liking ratings under
independent sampling
- a new optimization procedure which updates coordinates and
measurement constant parameters at the same time instead of separately.
- calculation of CAIC and BIC information criterion statistics
Dependent sampling has the provocative property of being able to
uniquely estimate a multidimensional solution with just one ideal object
using only liking rating data. Dependent sampling must, of
course, characterize the data as well as the model. Tests of
whether data fit a dependent or independent sampling model may be made
using the information criterion statistics provided by PROSCAL. Another advantage of
dependent sampling is that the variances on the real and ideal objects
are uniquely estimated, even when the only data available are liking
ratings.
The ability to evaluate data with only one ideal object is valuable
when testing whether a market is homogeneous or segmented. It is
also valuable when the traditional assumption that different segments
perceive products in the same way is suspect. This is a common
occurrence. Consumers who are diet conscious, for example,
frequently perceive product properties differently from consumers who
are not diet conscious.
Data sets illustrating single ideal sampling as well as multiple
ideal sampling and a variety of other probabilistic multidimensional
scaling scenarios are available from the Downloads page. The data
sets are described in the manuals. Manuals for both the SPlus and command line versions of Proscal
have been updated.
A forthcoming Journal of Mathematical Psychology paper
which describes the theory behind the new dependent
sampling models is available on the Publications page.
Feedback on Proscal5.0 is desired. Please send your comments to
mackay@proscal.com.
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