*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.

**Quickly diagnose missing data imputation problems**using diagnostic reports.**Replace missing data values with estimates**using a multiple imputation model.**Display and analyze patterns**to gain insight and improve data management.

## SPSS Missing Values Screenshots

## 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.

## 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.

## 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.

## 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.

**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.

### SPSS Missing Values resources

- Data sheet: IBM SPSS Missing Values (589KB)
Learn how IBM SPSS Missing Values helps you validate your research data and build better models.

- Trial software: IBM SPSS Statistics Desktop
Identify your best customers, forecast future trends, and perform advanced analysis.

- White paper: Seven Reasons You Need Predictive Analytics Today
Learn about the seven strategic objectives that can be attained by employing predictive analytics.

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Priority Code: Analytics