Data Cleaning Basics: Best Practices in Dealing with Extreme Scores

In quantitative research, it is critical to perform data cleaning to ensure that the conclusions drawn from the data are as generalizable as possible, yet few researchers report doing so (Osborne JW. Educ Psychol. 2008; 28: 1-10). Extreme scores are a significant threat to the validity and generalizability of the results. In this article, I argue that researchers need to examine extreme scores to determine which of many possible causes contributed to the extreme score. From this, researchers can take appropriate action, which has many laudatory effects, from reducing error variance and improving the accuracy of parameter estimates to reducing the probability of errors of inference.