Approximately 50% of cases are missing data on one of my predictor variables. With the default option selected (listwise treatment of missing data), the models produced are weak. This is probably because the listwise option reduces n substantially.
The alternative (pairwise exclusion), when selected, produces a strong model (the total variance explained is about 50%) with a number of significant predictors (the variable with 50% missing data is a significant predictor in this model).
However, this sounds a bit too optimistic. I've read that when pairwise exclusion is selected, SPSS will base degrees of freedom for significance testing on the number of cases with complete data (in this case, 32) rather than on the total number of cases. From what I understand, this means that the significant effects may be exaggerations.
Am I right to be concerned about the potential for exaggerated effects when pairwise exclusion is selected? Or are the parameter estimates (and the model as a whole) still trustworthy?