It's pretty simple, too, in Mathematica. Here's one way:

data = {{13.2, 200, 58, 21.2}, {10, 263, 48, 44.5}, {8.1, 294, 80, 31}, {8.8, 190, 50, 19.5}, {9, 276, 91, 40.6},
   {7.9, 204, 78, 38.7}, {3.3, 110, 77, 11.1}, {5.9, 238, 72, 15.8}, {15.4, 335, 80, 31.9}, {17.4, 211, 60, 25.8}};
pc = PrincipalComponents[data, Method -> "Correlation"];
k = Dimensions[data][[2]];
Table[Correlation[pc[[All, i]], data[[All, j]]], {i, k}, {j, k}] // MatrixForm

Good. I assume you must have access to SPSS and I also assume you'll be trying the same dataset on both SPSS and Mathematica as there are many times subtle but definite differences in algorithms, settings, assumptions, jargon, etc.

-0.60157 is what SPSS calls the component loading: the correlation of the first principal component and the first variable.

I haven't added in the "sanitizing" that SPSS does (like only giving the "most important" principal components and putting in blanks where the absolute value of the correlations are less than some threshold. On can add in those features but I'd argue you should only do that (if you're going to do that at all) after looking at all of the output.