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Nearly all quantitative analyses in higher education draw from incomplete datasets--a common problem with no universal solution. In the first part of this paper, we explain why missing data matter and outline the advantages and disadvantages of six common methods for handling missing data. Next, we analyze real-world data from 5,905 students across 33 institutions to document how one’s approach to handling missing data can substantially affect statistical conclusions, researcher interpretations, and subsequent implications for policy and practice. We conclude with straightforward suggestions for higher education researchers looking to select an appropriate method for handling missing data.