Missing Value Analysis

The Missing Value Analysis procedure performs three primary functions:

• Describes the pattern of missing data. Where are the missing values located? How extensive are they? Do pairs of variables tend to have values missing in multiple cases? Are data values extreme? Are values missing randomly?
• Estimates means, standard deviations, covariances, and correlations for different missing value methods: listwise, pairwise, regression, or EM (expectation-maximization). The pairwise method also displays counts of pairwise complete cases.
• Fills in (imputes) missing values with estimated values using regression or EM methods; however, multiple imputation is generally considered to provide more accurate results.

Missing value analysis helps address several concerns caused by incomplete data. If cases with missing values are systematically different from cases without missing values, the results can be misleading. Also, missing data may reduce the precision of calculated statistics because there is less information than originally planned. Another concern is that the assumptions behind many statistical procedures are based on complete cases, and missing values can complicate the theory required.