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 SPSS Missing Value Analysis 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 data validty, including survey researchers, social scientists, data miners, and market researchers.
Uncover missing data patterns
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 regression or expectation maximization algorithms (EM algorithms). SPSS Missing Value Analysis 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.
You can also use SPSS Missing Value Analysis to improve survey questions that you've identified as possibly troublesome or confusing, based on observed missing data patterns. You can even determine if missing variables for one variable are related to missing variables of another with the percent mismatch of patterns table. For example, respondents who didn't answer a question on income might also skip a question about education level. Use this information to enhance the quality of your surveys in the future by improving these questions.
Receive better summary statistics
Since summary statistics are often the starting point for other analyses, SPSS Missing Values Analysis allows you to adjust for missing data when working with them. Choose from four methods: listwise deletion, pairwise deletion, the EM algorithm, and covariance matrix.
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 reaching statistically significant results. 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. Choose from regression and EM algorithms to predict missing values based on data you already have.