Can I compare regression results with principal component analysis(PCA)

I'm trying to analyze survey data ( 31 response data, and 5 variables, dependent variable is non-parametric (distribution free)). I tried to use regression and PCA to select the most important independent variables to the dependent variable. My questions are:
1) Is it legitimate to view regression in the same way as PCA (methods for variable selection)?
2) Are there any sources to compare these two methods? For example: in terms of the easiness to interpret the results I feel that regression is easier than PCA, or if the method will give the same results if we use one-third or two-third the data points (insensitive to the data point change).

HELP please. Thanks:)


TS Contributor
PCA isn't a method for assessing "importance" (however defined)
of variables with regard to their predictive power, at least AFAIK.
It's a data reduction tool.

With kind regards


BTW, only 31 subjects while there are 5 variables wont't give
you generalizable results in most cases, regardless of what your
method of analysis will be.


Fortran must die
Structural equation models use a form of regression, but PCA does not. It's components do reflect variance in the variables, but unlike regression it is not assumed that one variable is explaining another, the relationship can flow both ways (that is rather than X-> Y X->Y and Y->X).