Missing data: the hidden problem, a white paper that shows how missing data can affect your analysis and resulting decisions, is available for review!

Quickly and easily assess the magnitude of each pattern of missing data using the Tabulated Patterns table. In this example, you can see the most common missing pattern is income. You can also tell that non-professionals were much less likely to answer a question on income. 

Missing data can seriously affect your results. When you ignore missing data or assume that excluding missing data from calculations is sufficient, you risk getting invalid results. 
SPSS Missing Value Analysis is a critical tool for anyone concerned about the validity of their data, including survey researchers, social scientists, data miners and market researchers. 

Easily examine your data from several different angles with six diagnostic reports to uncover missing data patterns. And, then estimate summary statistics and impute missing values through statistical algorithms. 

Quickly and easily diagnose your missing data 
Explore your data with six flexible diagnostic reports to assess if missing data can impact your conclusions and to better understand the characteristics of the missing data. 
You can quickly diagnose if you have a serious missing data problem with the Data Patterns report. This report gives you an overview of your data case by case. It helps you determine the extent of missing data and gives you a snapshot of each type of missing value and any extreme values for each case. 

Use the flexible Separate Variance t-test and Crosstab of Categorical Variables tables to discover if significant differences exist between respondents and nonrespondents. These reports help you decide if missing data can cause problems in your analysis. 

The Tabulated Patterns report summarizes each missing data pattern and highlights which set of variables make up the pattern. For example, quickly see that 98 of the 100 cases did not respond to the set of questions about preferred features and price. 

Enhance the quality of future data by improving any survey questions which may be confusing or identified as a trouble spot by a missing data pattern. And, confidently determine if missing values for one variable are related to missing values of another variable with the Percent Mismatch of Patterns table that displays the relationships between variables with missing data. For example, respondents who did not answer the income question also did not answer the question about education. 

Get better summary statistics 
Summary statistics of your data often are the starting point for other analyses, such as factor, regression or ANOVA. Get more reliable results by using summary statistics which are adjusted for missing data. 
Choose from four methods, Listwise Deletion, Pairwise Deletion, EM or Regression to estimate the means, correlation matrix or covariance matrix. 

Easily replace missing values with estimates 
Improve the likelihood of finding statistically significant results by using all your data instead of limiting your analysis to complete cases. Choose the powerful EM or Regression algorithm to predict missing values based on data you already have. 
Draw more valid conclusions by removing hidden bias from your data by replacing missing values with estimates so all groups are represented in your analysis - even those with poor responsiveness. 

System requirements 
SPSS Base 9.0 for Windows 
2MB of additional hard disk space