Best Practices in Data Cleaning: A Complete Guide to Everything You Need to Do Before and After Collecting Your Data

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Too Fast, too Straight, too Weird: Non-Reactive Indicators for Meaningless Data in Internet Surveys (2019, background): “The academic community is just beginning to establish standards on how to identify and handle potentially problematic records (Osborne, 2013).”
Modeling factors explaining the acceptance, actual use and satisfaction of nurses using an Electronic Patient Record in acute care settings: An extension of the UTAUT (2015, positive): “Mean substitution of missing data was performed by creating composite scores [59] based on the average of 75% of the remaining items for each construct.”
Finding latent groups in observed data: A primer on latent profile analysis in Mplus for applied researchers (2020, methodology): “Step one, as with all analyses, the data should be cleaned for analysis and checked for standard statistical assumptions (e.g., normality of continuous variables, independence of observations, etc.; see Osborne, 2012).”
Burnout, Stress, and Direct Student Services Among School Counselors (2016, methodology): “, data points three or more standard deviations from the mean) using converted z-scores (Osborne, 2012), which resulted in identifying 21 cases that had at least one variable with an extreme outlier.”