# Pairwise deletion in multiple regression

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?

• Can you provide a citation for the statement you read about SPSS's degrees of freedom being based on the listwise N? Also, if this is true, it will dampen, not exaggerate, significance as compared with results based on the total N. – rolando2 Sep 29 '12 at 11:29
• For the pairwise option in regression, the SPSS manual reads: "Cases with complete data for the pair of variables being correlated are used to compute the correlation coefficient on which the regression analysis is based. Degrees of freedom are based on the minimum pairwise N." – Archaeopteryx Sep 29 '12 at 13:32

When you have so much missing data, the first concern is why the data are missing. They can be missing completely at random (MCAR), missing at random (MAR) or not missing at random (NMAR). Searching on missing data here, or on any of those terms in Google, should give you lots of information.
I don't know about SPSS capacity with regard to multiple imputation (I am not an SPSS user) but both R and SAS have excellent abilities in this regard.