SPSS Statistics - Missing Values

Quickly diagnose missing data imputation problems

• Examine data from different angles using six diagnostic reports.
• Diagnose missing data using the data patterns report, which provides a case-by-case overview of your data.
• Determine the extent of missing data and any extreme values for each case.

Replace missing data values with estimates

• Understand missing patterns in your data set and replace missing values with plausible estimates.
• Benefit from an automatic imputation model that chooses the most suitable method based on characteristics of your data, or customize your imputation model.
• Model the individual data sets that are created, using techniques such as linear regression or expectation maximization algorithms, to produce parameter estimates for each.
• Obtain final parameter estimates by pooling estimates and computing inferential statistics that take into account variation within and between imputations.

Display and analyze patterns

• Display missing data for all cases and all variables using the data patterns table.
• Determine differences between missing and non-missing groups for a related variable with the separate t-test table.
• Assess how much the missing data for one variable relates to the missing data of another variable using the percent mismatch of patterns table.