If this is not the first book or article by the first author that you have come across, you might know that he has been a constant (perhaps tiresome) advocate of the argument that data is not ready to analyze until it is clean and missing data has been dealt with. In fact, he wrote an entire book about all the different—legitimate—things a researcher can do to improve the quality of their data and the results that come from analysis of those data.

Exploratory factor analysis is no exception, but there are different issues, and different priorities when dealing with EFA. Residuals, a common tool used to identify outliers in inferential statistics, contradict the exploratory nature of EFA[1] and thus they cannot be used for EFA. Instead, a number of other tools and techniques are used to identify overly influential cases. Missing data is also increasingly problematic to EFA. The factor analysis model cannot handle missing data and deletes such cases by default. This process can introduce systematic error and bias into the results. Therefore, missing data should be reviewed and potentially dealt with, via various methods, prior to EFA.

In this chapter we aim to provide a brief look at the issue of data cleaning for EFA. Specifically, we seek to discuss two particular threats to EFA—outliers and missing data—and the methods for dealing with them. A thorough review of this topic and the associated SAS procedures is beyond the scope of this book. Interested readers are encouraged to seek out additional information on their own.