Build better models when you estimate missing data

IBM® SPSS® Missing Values software is used by survey researchers, social scientists, data miners, market researchers and others to validate data. The software allows you to examine data to uncover missing data patterns, then estimate summary statistics and impute missing values using statistical algorithms.

With SPSS Missing Values software, you can impute your missing data, draw more valid conclusions and remove hidden bias.

Multiple imputation

The purpose of multiple imputation is to generate possible values for missing values, thus creating several "complete" sets of data. Analytic procedures that work with multiple imputation datasets produce output for each "complete" dataset, plus pooled output that estimates what the results would have been if the original dataset had no missing values. These pooled results are generally more accurate than those provided by single imputation methods. In the highlighted cells, the missing values have been imputed.

Multiple imputation

Summary of missing values

The overall summary of missing values displays three pie charts that show different aspects of missing values in the data. The Variables chart shows that each of the 10 analysis variables has at least one missing value on a case. The Cases chart shows that 738 of the 1000 cases has at least one missing value on a variable. The Values chart shows that 1168 of the 10,000 values (cases × variables) are missing.

Summary of missing values

Variable summary

The variable summary is displayed for variables with at least 10 percent missing values, and shows the number and percent of missing values for each variable in the table. It also displays the mean and standard deviation for the valid values of scale variables, and the number of valid values for all variables.

Variable summary

Missing value patterns

A patterns chart displays missing value patterns for the analysis variables. Each pattern corresponds to a group of cases with the same pattern of incomplete and complete data.

Missing value patterns

Patterns bar chart

When patterns are requested a companion bar chart displays the percentage of cases for each pattern.

Patterns bar chart

SPSS Missing Values Screenshots

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Multiple imputation

The purpose of multiple imputation is to generate possible values for missing values, thus creating several "complete" sets of data. Analytic procedures that work with multiple imputation datasets produce output for each "complete" dataset, plus pooled output that estimates what the results would have been if the original dataset had no missing values. These pooled results are generally more accurate than those provided by single imputation methods. In the highlighted cells, the missing values have been imputed.

Multiple imputation

Summary of missing values

The overall summary of missing values displays three pie charts that show different aspects of missing values in the data. The Variables chart shows that each of the 10 analysis variables has at least one missing value on a case. The Cases chart shows that 738 of the 1000 cases has at least one missing value on a variable. The Values chart shows that 1168 of the 10,000 values (cases × variables) are missing.

Summary of missing values

Variable summary

The variable summary is displayed for variables with at least 10 percent missing values, and shows the number and percent of missing values for each variable in the table. It also displays the mean and standard deviation for the valid values of scale variables, and the number of valid values for all variables.

Variable summary

Missing value patterns

A patterns chart displays missing value patterns for the analysis variables. Each pattern corresponds to a group of cases with the same pattern of incomplete and complete data.

Missing value patterns

Patterns bar chart

When patterns are requested a companion bar chart displays the percentage of cases for each pattern.

Patterns bar chart
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Quickly diagnose missing data imputation problems

Replace missing data values with estimates

Display and analyze patterns

SPSS Missing Values resources

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Build better models when you estimate missing data

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