Dealing with Missing Data and Outliers

The earlier chapters showed you how to read common file formats and define Variable Properties. In any project, as you pull together the data that helps you address your business question or research question, you must spend some time gaining an understanding of your data via a data audit. Simple procedures such as Frequencies, Descriptives, or Examine can give you a summary understanding of each variable via statistical and graphical means. In addition, the data audit should focus on unusual/extreme values and the nature and extent of missing data.

The topics covered in this chapter include the following:

Outliers:

• Frequencies for a histogram and percentile values
• Descriptives for standardized scores
• The Examine procedure for extreme values and boxplot
• Detecting multivariate outliers using the Regression procedure

Missing data:

• Missing values in Frequencies
• Missing values in Descriptives
• Missing value patterns
• Replacing missing values