PASW Missing Values | Estimate Missing Data

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 reaching invalid and insignificant results. To ensure that you enter the data analysis stage using data that takes missing values into account, make PASW Missing Values (formerly called SPSS Missing Value Analysis™) part of your data management and preparation step.

PASW Missing Values, is a critical tool for anyone concerned about data validity, including survey researchers, social scientists, data miners, and market researchers.

Uncover missing data patterns

With PASW Missing Values, 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 regression or expectation maximization algorithms (EM algorithms). PASW Missing Values helps you to:

Diagnose if you have a serious missing data imputation problem
Replace missing values with estimates—for example, impute your missing data with the regression or EM algorithms

Quickly and easily diagnose your missing data

Quickly diagnose a serious missing data problem using the data patterns report, which provides a case-by-case overview of your data. This report helps you determine the extent of missing data; it displays a snapshot of each type of missing value and any extreme values for each case.

Use multiple imputation to replace missing data values

In PASW Missing Values 17.0, a new multiple imputation procedure will help you understand patterns of “missingness” in your dataset and enable 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 allowing you to customize your imputation model.

Several complete datasets are generated (typically, three to five), each with a different set of replacement values. Next, you can model the individual datasets using the usual techniques, such as linear regression, to produce parameter estimates for each dataset. Then obtain final parameter estimates. This involves pooling the individual sets of parameter estimates obtained in step two and computing inferential statistics that take into account variation within and between imputations.

Analysis of the individual datasets and pooling of the results are supported via select existing PASW Statistics procedures such as REGRESSION. When operating on datasets with imputed values, existing procedures will automatically produce pooled parameter estimates.

Reach more valid conclusions

Replace missing values with estimates and increase the chance of receiving statistically significant results. Remove hidden bias from your data by replacing missing values with estimates to include all groups in your analysis—even those with poor responsiveness.

PASW Missing Values is available in English, Japanese, French, German, Italian, Spanish, Chinese, Polish, Korean, and Russian.

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