Data error prevention and cleansing: A comprehensive guide for instructors of statistics and their students

The proper analysis of data is predicated on the existence of a data set containing valid responses. There are many sound techniques that should be employed to minimize data errors, and to cleanse data sets. The purpose of this article is to provide instructors and their students with an overview of the mechanics of data capture; the metadata framework; outlier detection and treatment; and contemporary solutions for missing data.