In other situations, missing values may be treated as a valid category. The choice between these two types of deletion is not relevant when only one variable is being analyzed. The choice between pairwise and listwise deletion of records is limited. Correlations are based on all data available for each pair of variables. Note that the means and standard deviations computed when pairwise deletion is specified are based on all available data for each variable. This can occur because when correlations are computed using different cases, the resulting patterns can be ones that are impossible to produce with complete data. That is, it may have negative eigenvalues, which can create problems for various statistical analyses. For example, a correlation matrix computed using pairwise deletion may not be positive semidefinite. However, each computed statistic may be based on a different subset of cases. Pairwise deletion allows you to use more of your data. A case may have a missing value for VAR1, but this does not prevent some statistical procedures from using the same case to analyze variables VAR2 and VAR3. A case may contain 3 variables: VAR1, VAR2, and VAR3. The procedure cannot include a particular variable when it has a missing value, but it can still use the case when analyzing other variables with non-missing values. Pairwise deletion occurs when the statistical procedure uses cases that contain some missing data.
The analysis is only run on cases which have a complete set of data. In listwise deletion a case is dropped from an analysis because it has a missing value in at least one of the specified variables. A case may be omitted from an analysis because it contains one or more missing values in the variables being analyzed.
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