If, for example, you are running a corrrelation, and you have missing data from a participant on Q.5, a pairwise exclusion will include the participant's entire set of data in the correlation EXCEPT Q.5. In a listwise exclusion, the participant's entire case would be dropped, leaving you with too small a number of participants. So SPSS really takes care of it for you.

The other thing you can do is mean substitution. IE, you can average the scores of the rest of the cases on that particular item, and give that participant that score. This however, will reduce variance.

There is also on SPSS the option of expectation maximisation: Go to analyse> missing value analysis> EM. But you have to ensure normality of distribution to use this option. This doesn't effect variance as much as mean substitution.

Hope this helps.