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SPSS Missing Value Analysis
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 reaching invalid and insignificant results. Ensure you enter the data analysis
stage using data that takes missing values into account with SPSS Missing Value Analysis 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 data validity 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 the expectation maximization (EM) or Regression 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, EM, 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 the EM and regression algorithms to predict missing values based on data you already have.
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