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Missing Values
Missing Values
The IBM® SPSS® Missing Values module helps you manage missing values in your data analysis 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 biases.
- 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 the IBM® SPSS® Complex Sampling and Testing add-on for subscription plans.
Schedule time to discuss how SPSS Missing Values can support your business needs.
Feature spotlights
The multiple imputation procedure helps you understand the patterns of missing data in your dataset 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 enabling you to customize your imputation model.
You can generate possible values for missing values to create several "complete" datasets. 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.
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 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.
Technical details
- For on-premises: purchase the Professional edition.
- For subscription plans: purchase the Complex Sampling and Testing add-on.
- Processor: 2 GHz or faster
- Display: 1024*768 or higher
- Memory: 4 GB of RAM required, 8 GB of RAM or more recommended
- Disk space: 2 GB or more