R help - Dec 2012 - Fwd: How to calculate different groups of varialbes importance level?

Dear R user!
I have a small question!
I have calculated the relative importance of the variables.

However I would like to compare the relative importance of two different
groups of variables (i.e Strategy and industry)

For example let me say that strategy has 2 sub varialbes and industry has
four different variables!

Can I simply add the importance of those four industry variables importance
over each other  and say that the importance level of industry is the total
of those four varibales' importance?
Can I also do the same thing and add the importance of two strategic
variables and have a strategic level importance?

After these simple calculation, can I compare the importance of those
groups?

Thanks for the kind help
Best regards

--
 Solmaz Filiz KARABAG




--
 Solmaz Filiz KARABAG

        [[alternative HTML version deleted]]





-- 
 Solmaz Filiz KARABAG

	[[alternative HTML version deleted]]

On 12/02/2012 08:18 PM, Solmaz Filiz KARABA? wrote:
> Dear R user!
> I have a small question!
> I have calculated the relative importance of the variables.
>
> However I would like to compare the relative importance of two different
> groups of variables (i.e Strategy and industry)
>
> For example let me say that strategy has 2 sub varialbes and industry has
> four different variables!
>
> Can I simply add the importance of those four industry variables importance
> over each other  and say that the importance level of industry is the total
> of those four varibales' importance?
> Can I also do the same thing and add the importance of two strategic
> variables and have a strategic level importance?
>
> After these simple calculation, can I compare the importance of those
> groups?
>
Hi Solmaz,
There are two ways to combine related variables that are generally 
accepted. The cold, hard, arms-length method is to see whether those 
variables are covarying to the extent that we can legitimately infer 
that an underlying variable is responsible for that covariance. Say that 
your strategy measures 1) how long you spent developing that strategy 
and 2) how many sources of information you consulted. These two measures 
are likely to involve the underlying behavior of extensive preparation 
for developing a strategy rather than just having a couple of beers and 
flipping a coin. So the beer-flippers are likely to score low on both 
measures and the slow swots are likely to score high and principal 
components analysis or similar will get you through.

The second method is to convince people that they go together. Instead 
of applying the black box of mathematic analysis, one shines the clear 
light of logic upon the problem. It is apparent to anyone with the 
normal quota of neurons that expended time and verified sources of 
information are more likely to be applied together in developing a good 
strategy and so on. If you are important or persuasuve enough, you may 
get away with mere assertion. If not, you must appeal to the authority 
of others, particularly those who have already demonstrated some 
quantitative association between the measures.

Reality usually involves performing the first method, and if this does 
not produce the desired result, trying to find support in the literature 
for the result you would like. You can of course just baldly state that 
you are combining the variables in a particular way beacuse you think it 
makes sense and apply the empirical test of whether anyone buys your story.

Jim