On a set of 4 psychometric 5-point likert scales I performed PCA and
I saved the new PC scores. Then I rescaled the PC scores in a range
where min=0 and max=100. My goal was to perform a regression using
the rescaled PC's as independent variables in a binary logistic
regression so that betas would have meaningful interpretation.

I have some doubts about the result as I couldn't find any relative
article.

A brief description of what I did is presented below.
Any suggestion is greatly appreciated.



-The Component Score Coefficient Matrix is:

PC1 PC2
x1 0.80 -0.41
x2 0.55 -0.15
x3 -0.11 0.53
x4 -0.43 0.84

-The maximum and minimum values of the standardized original scales
are:

min max
zx1 -1.02 1.76
zx2 -0.97 1.86
zx3 -1.28 1.33
zx4 -1.26 1.30


-the maximum PC-values are calculated by multiplying positive factor
score coefficients with the maximum z-scores and negative coefficients
with the minimum z-scores
-the minimum PC-value are calculated by multiplying positive factor
score coefficients with the minimum z-scores and negative coefficients
with the maximum z-scores

-so, the maximum and minimum for PC1 are calculated as:

max(PC1)=0.80*1.76+0.55*1.86-0.11*(-1.2-0.43*(-1.26)=3.12
min(PC1)=0.80*(-1.02)+0.55*(-0.97)-0.11*1.33-0.43*1.30.=-2.06.

-and finally, the rescaled PC1 [0,100] is calculated as:

newPC1=0+100*((PC1-min(PC1)) / (max(PC1)-min(PC1)).