I've changed my general approach a little bit. Insteadt of using MI for doing the PCA I've used an EM-Algorithm to estimate just the covariances/correlations of my original sample directly, without making a detour over imputing missings. Then, I used the estimated correlation matrix as direct input into a single PCA. With the resulting factors I'm indeed calculating factor scores for every single of my five imputed data sets, followed by five logistic regressions. Finally, I'm combining the results of all five logistic regressions. That's it and I think it's statistically the best solution. Originally I had wished to use a Full Information Maximum Likelihood Estimation Algorithm for estimating my covariances but I couldn't find a properly intergrated syntax or program so I used the EM-Estimation option in SPSS (without imputing here, because those imputations are biased) - for imputing I used an R library (Amalia).+

Greetings,

Marina