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Data preprocessing is an integral step prior to analyzing data in psychological science, with implications for its potentially guiding policy. This article reports how psychological researchers address data preprocessing or quality concerns, with a focus on aberrant responses and missing data in self-report measures. 240 articles were sampled from four journals: Psychological Science, Journal of Personality and Social Psychology, Developmental Psychology, and Abnormal Psychology from 2012 to 2018. Nearly half of the studies did not report any missing data treatment (111/240; 46.25%), and if they did, the most common approach was listwise deletion (71/240; 29.6%). Studies that remove data due to missingness removed, on average, 12% of the sample. Likewise, most studies do not report any aberrant responses (194/240; 80%), but if they did, they classified 4% of the sample as suspect. Most studies are either not transparent enough about their data preprocessing steps or may be leveraging suboptimal procedures. Recommendations can improve transparency and data quality.