- Canonical analysis computes weights
for two sets of variables, A and B, such that it
maximizes the simple product moment correlation
between the weighted composite scores for set A and set
B.
- The canonical correlation is
interpreted exactly as a simple product-moment
correlation.
- The weights on both sides of the
equation (canonical variates) are analogous to
b-coefficients in linear regression.
- More than one canonical
correlation is produced for each analysis.
- The number of canonical
correlations (roots) equals the larger of the two
sets of variables.
- The first canonical correlation
is the largest; successive ones are progressively
smaller.
- Scores produced from canonical
variates are unrelated across roots.
- Whereas factor analysis can be used
appropriately as a tool for exploratory research,
canonical analysis needs to be guided by
theory.
- Canonical analysis is a tool for
testing theory; not developing measures.
- Alternative measures of the same
concept should not be included in a canonical
variate.
- Instead, combine the measures to
form a scale, compute its reliability, and use the
scale in analysis.
- Consider canonical analysis for
relating variables grouped under "umbrella
concepts."
- Umbrella concepts group together
variables that share some conceptual underpinnings but
that express distinct phenomena.
- Consequently, the concepts are not
closely related--in the sense of
unidemensionality.
- Some examples of umbrella
concepts:
- International relations,
which embraces
- war and peace
- armed conflicts
- economic trade
- and so on.
- Congressional behavior,
which embraces
- party unity in congressional
voting
- introduction of
bills
- committee
participation
- Political party
organization
- centralization of
power
- structural
complexity
- Political party
performance
- winning
elections
- providing services for
members
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