Missing values that fail to be appropriately accounted for may lead to reduced statistical power, biased estimators, reduced representativeness of the sample, and incorrect interpretations and conclusions (Gorelick, 2006). The current study provided an ontological perspective of data manipulation by explaining how statistical results can fundamentally change depending on specific data modification methods. This has consequential implications, specifically in higher education, that depend on quantifiable methodologies to substantiate practices through evidence based policy making (Gillborn et al., 2018; Sindhi et al., 2019). The results of the current study exposed how examining patterns of data missingness can have critical implications on student retention initiatives including intervention programs, identification of high-risk students, and funding opportunities for support programs. It is imperative for both data scientists and data stakeholders to be critically aware of what data they collect, report, and utilize from the variable selection to statistical methodologies. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com.bibliotheek.ehb.be/en-US/products/dissertations/individuals.shtml.]