I have 8 variables that I want to combine into one performance score. I am doing PCA and taking the first three components (~62% of variance), then adding the three scores for each record from these components to get one final score. My question: four variables load high on the first component, three load high on the second component, and one variable loads high on the third component. Does that mean that the last variable that is loading high on the third component will have more impact on the final score than the other variables? I am asking since I want equal representation for all variables in my final score. Thank you for your help in advance.
Yes, it probably does mean that the last variable will have a higher impact (I write "probably", as it depends on the magnitude of the the loadings and the standard deviation of the original variables).
The way to achieve your requirement of "equal representation for all variables in my final score" is most readily met by standardizing each of the variables (e.g., dividing each by its standard deviation) and then summing them. But, by doing PCA you are implicitly saying that you do not want to have each variable have the same impact, and are instead trying to ensure that each of three dimensions in the underlying dimensional space are equally represented.
To add some more complication: a three component solution only explaining 62% of the variance of 8 variables is not a strong fit, so some of your loadings are probably pretty poor, making the whole analysis a but suspect.