Are the "factor loadings" you referred here = estimates in the model output?
Then I have another related question. In general, if A = alpha F + Int1 ... (1); and F = beta A + int2 ... (2). Then: beta = R-square from (1) /alpha.
By saying "how well the a's, b's, and c's measure the F's" using the loadings, kind of equally using 1/loadings to say how well the a, b and c's "predict" F, am I right? Since the scale is inversed, I may get opposite conclusions. For example; if a1 = 10 f1; a2 = .1 f1; with R^2 = 0.5. Looks like a2 is a poor measure for f1 in this case. However, if you rewrite the above to:
Yes, loadings are the "f BY a" estimates given in the output.
How well an indicator measures a factor is related to the R-square in the indicator as a function of the factor, so not only the loading size - you will get this information when requesting Standardized in the Output command. The way an indicator contributes to the factor score estimation is seen in the factor score coefficient matrix which is a function of all the parameter estimates of the model - see the matrix C expression in equation (226) in appendix 11 of the technical appendices on the web site, and also general factor analysis books.