Regarding your interest in discussion of grid analysis I will
offer a few comments as I took a somewhat different and critical
approach to this subject. In research I undertook, SPSS was used
to perform multidimensional scaling on 40 grids. To interpret the
output 1)graphs of various constructs were used to determine if
the dimensional interpretations which I made separated the
elements in a similar manner, 2)SPSS principal component analyses
of individual grid data were used to see if individuals with the
largest weights on a particular dimension had similar types of
constructs which loaded on the principal component. Also output
from G-Pack, i.e., construct correlations, was used to check
interconnections between constructs. For example, a person might
have constructs which did not seem relevant to the interpretation
which was made, though the constructs might all be highly
correlated to another construct which was the link between the
constructs.
I believe the above approach enhanced the validity of the
dimensional interpretations ... though apologise to people who
are not familiar with the "fun" of mds as my brief description
of method has been limited. In formulating my use of mds I found
some of the feedback I received from contributors to this list
most helpful, particularly as there is very little in the grid
literature on mds and related approaches.
Regards,
Bob Green
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