The IBM® SPSS® Missing Values module helps you manage missing values in your data and draw more valid conclusions. Uncover the patterns behind missing data, estimate summary statistics and impute missing values using statistical algorithms. The module helps you build models that account for missing data and remove hidden bias. Survey and market researchers, social scientists, data miners and other professionals rely on IBM SPSS Missing Values to validate their research data.
This module is included with SPSS professional and premium packages. You can also buy it to add to base and standard packages. This module is included in the SPSS professional edition for on premises and in the “Complex sampling and testing” add-on for subscription plans.
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The multiple imputation procedure helps you understand the patterns of missing data in your data set and enables you to replace missing values with plausible estimates. It offers a fully automatic imputation mode that chooses the most suitable imputation method based on characteristics of your data, while also allowing you to customize your imputation model.
You can generate possible values for missing values to create several "complete" sets of data. Analytic procedures that work with multiple imputation data sets produce output for each "complete" data set, plus pooled output that estimates what the results would have been if the original data set had no missing values. These pooled results are generally more accurate than those provided by single imputation methods.
You can quickly diagnose a serious missing data problem using the overall summary of missing values report. The missing values pattern report provides a case-by-case overview of your data. It displays a snapshot of each type of missing value and any extreme values for each case. The overall summary of missing values report can display pie charts that show different aspects of missing values in the data.
The variable summary is displayed for variables with at least 10% missing values, and shows the number and percent of missing values for each variable in a 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. 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.