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Fill in the Blanks for Improved Data Management
Expand SPSS Base's capabilities with SPSS Missing Value Analysis. 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, provides you with procedures for data management and preparation. Also, it easily plugs into SPSS Base ensuring you can work seamlessly in the SPSS for Windows 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
- Expectation maximization (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 the ability to:
- 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
- And more
This separate variance t test table defines two groups of cases: those with data on income and those that are missing data on income. Then, the separate variance t test table tests to see if these two groups are different from each other on a series of variables. This table shows that people with missing data on income are more likely to have a non-professional occupation, more likely to be female, more likely to be married, and have a larger family than people who reported information on their family income.
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