# Calculating principal component scores after PC analysis

I am carrying out a study to find out meteorological patterns using daily met observations including around 30 met parameters (each day is a case with 30 variables). My methodology includes carrying out a PCA:

1. To reduce 30 variables to smaller number of PCs.

2. Find out the PC scores for all days in the ten years.

As first I standardized all 30 variables using SPSS function ANALYSE>>> DESCRIPTIVE. Now that I have got 6 PCs explaining 80% of variance of original data, I have to calculate the PC scores. My questions are as under;

1. Should I use Component Matrix OR Rotated Component Matrix for PC score calculation?

2. Should I multiply the component/Rotated component matrix with original variable matrix (un-standardized) OR i should multiply it with standardized form of original variable matrix?