I'm having a hard time seeing how this would make any sense. Is this ever done? How does one go about it?

- Thread starter lsrms3
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I'm having a hard time seeing how this would make any sense. Is this ever done? How does one go about it?

Hi, can you explain how I would do that? Thanks!!

the right way would be to use the principal component analysis but you might want to see if a simpler method would work. Transform both variables by standardising them x -> (x-Meanx)/Stdx then combine the two standardized variables into one (e.g. by taking the average). The reason you can do this is that by standardizing the variables you turned them into simple numbers without dimensions, so you are not combining apples with oranges. If you "know" that one variable is more important than the other you can use a weighted average as bugman suggested, but you should be able to argue for your choice of weights based on domain knowledge. E.g. "the choice of 0.2 0.8 leads to significant results" is NOT a good argument

regards

Hi,

the right way would be to use the principal component analysis but you might want to see if a simpler method would work. Transform both variables by standardising them x -> (x-Meanx)/Stdx then combine the two standardized variables into one (e.g. by taking the average). The reason you can do this is that by standardizing the variables you turned them into simple numbers without dimensions, so you are not combining apples with oranges. If you "know" that one variable is more important than the other you can use a weighted average as bugman suggested, but you should be able to argue for your choice of weights based on domain knowledge. E.g. "the choice of 0.2 0.8 leads to significant results" is NOT a good argument

regards

the right way would be to use the principal component analysis but you might want to see if a simpler method would work. Transform both variables by standardising them x -> (x-Meanx)/Stdx then combine the two standardized variables into one (e.g. by taking the average). The reason you can do this is that by standardizing the variables you turned them into simple numbers without dimensions, so you are not combining apples with oranges. If you "know" that one variable is more important than the other you can use a weighted average as bugman suggested, but you should be able to argue for your choice of weights based on domain knowledge. E.g. "the choice of 0.2 0.8 leads to significant results" is NOT a good argument

regards