Missing data can seriously affect your models – and your results. Ignoring missing data, or assuming that excluding missing data is sufficient, risks reaching invalid and insignificant results. IBM SPSS Missing Values provides three main functions:
- It describes the pattern of missing data. Where are the missing values located? How extensive are they? Do pairs of variables tend to have values missing in multiple cases? Are data values extreme? Are values missing randomly?
- It provides estimates of statistics like means, standard deviations and correlations for data series that contain missing values.
- It fills in (imputes) missing data with estimated values using special methods like regression or EM (expectation-maximization).
IBM SPSS Missing Values helps address several concerns caused by incomplete data. By investigating patterns of missing data it can address questions such as ‘Why are the data missing?’. Moreover, the means estimation procedures address questions such as ‘How does the missing data affect summary statistics?’ and the imputation procedures are able to address questions such as ‘What are the likely values for the missing data?’
Using IBM SPSS Missing Values you can:
- Easily examine data from several different angles using one of six diagnostic reports, then estimate summary statistics and impute missing values
- Quickly diagnose serious missing data imputation problems
- Replace missing values with estimates
- Display a snapshot of each type of missing value and any extreme values for each case
- Remove hidden bias by replacing missing values with estimates to include all groups – even those with poor responsivenes
Operating systems supported: Windows, Mac, Linux