Create higher-value data and build better models when you estimate missing data

Missing data can seriously affect your results. If you ignore missing data or assume that excluding missing data is sufficient, you risk getting invalid and insignificant results. Ensure you enter the data analysis stage using data that takes missing values into account with SPSS Missing Value Analysis^TM as part of your data management and preparation step. SPSS Missing Value Analysis, an SPSS add-on module, is a critical tool for anyone concerned about the validity of data, including survey researchers, social scientists, data miners and market researchers.

With SPSS Missing Value Analysis, you can easily examine data from several different angles using one of six diagnostic reports to uncover missing data patterns. You can then estimate summary statistics and impute missing values through statistical algorithms. SPSS Missing Value Analysis helps you to:

• Diagnose if you have a serious missing data problem

• Replace missing values with estimates, for example, impute your missing data with EM or Regression algorithms

Improve the likelihood of finding statistically significant results
Use all of your data instead of limiting your analysis to complete cases. Easily replace missing values with estimates and increase your chance of getting statistically significant results. Choose from the EM and regression algorithms to predict missing values based on data you already have.

You can also 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.

Fill in the blanks when you use SPSS Missing Value Analysis for data management

Expand SPSS® Base's capabilities with SPSS Missing Value Analysis^TM. Make better decisions about your data when you can fill in the blanks to create higher-value data and build better models. SPSS Missing Value Analysis, an SPSS add-on module, gives you procedures for data management and data prep. Also, it easily plugs into SPSS Base ensuring you can work seamlessly in the SPSS environment.

SPSS Missing Value Analysis has the statistics you need to fill in missing data:

• Univariate: compute count, mean, standard deviation and standard error of mean for all cases excluding those containing missing values, count and percent of missing values and extreme values for all variables

• Listwise: compute mean, covariance matrix and correlation matrix for all quantitative variables for cases excluding missing values

• Pairwise: compute frequency, mean, variance, covariance matrix and correlation matrix

• EM Algorithm

  • Estimate the means, covariance matrix and correlation matrix of quantitative variables with missing values, assuming normal distribution, t-distribution with degrees of freedom or a mixed-normal distribution with any mixture proportion and any standard deviation ratio

  • Impute missing data and save the completed data as a file

• Regression Algorithm

  • Estimate the means, covariance matrix and correlation matrix of variables set as dependent; set number of predictor variables; set random elements as normal, t, residuals or none

  • Impute missing data and save completed data as file

SPSS Missing Value Analysis also has features that enable you to analyze patterns and manage data, including:

• Display missing data and extreme cases 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 missing data for one variable relates to the missing data of another variable using the percent mismatch of patterns table

• Handle all character variables as categorical variables

• And more

SPSS Missing Value Analysis^TM system requirements

• SPSS® Base

• 1MB hard drive space

• Other system requirements vary according to platform