OUTLIER DETECTION AND TREATMENT IN I/O PSYCHOLOGY: A SURVEY OF RESEARCHER BELIEFS AND AN EMPIRICAL ILLUSTRATION

By J. M. Orr, P. Sackett, Cathy L. Z. Dubois. 2006

201 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Best-Practice Recommendations for Defining, Identifying, and Handling Outliers (2013, background): “…(i.e., an error in the sampling procedure), lying outside the possible range of values, errors in observation, errors in recording, errors in preparing data, errors in computation, errors in coding, or errors in data manipulation (Kutner et al., 2004; Orr et al., 1991; Tabachnick & Fidell, 2007).”
The power of outliers (and why researchers should ALWAYS check for them) (2004, background): “When the data points are suspected of being legitimate, some authors (e.g., Orr, Sackett, & DuBois, 1991 ) argue that data are more likely to be representative of the population as a whole if outliers are not removed.”
Improving your data transformations: Applying the Box-Cox transformation (2010, methodology): “While researchers tend to report favoring "eyeballing the data," or visual inspection of either the variable or the error terms (Orr, Sackett, & DuBois, 1991), more sophisticated tools are available, including tools that statistically test whether a distribution deviates significantly from a…”