The novel coronavirus disease 2019 (COVID-19) pandemic has created an unprecedented set of obstacles for schools and exacerbated existing structural inequalities in public education. In spring 2020, as schools went to remote learning formats or closed completely, end-of-year assessment programs ground to a halt. As a result, schools began the 2020-2021 school year without student assessment data, which typically play a role in selecting students for specialized programming or placing students into courses. Although conceptual research has emerged to support school and district decision-making regarding assessment during the pandemic, there has been relatively little empirical research to help guide schools and school districts on handling the impacts of the pandemic on the availability and interpretability of assessment data. To address this gap, the authors of this report provide empirical evidence to inform schools' and districts' approaches to course placement in the absence of end-of-year assessment data. The authors compare and contrast three potential strategies that use older assessment data to estimate missing test scores: simple replacement, regression-based replacement, and multiple replacement. The authors examine the ways in which the pandemic may have influenced the consistency of decision-making under these strategies and the extent to which these strategies work equally well for all students, regardless of student race and ethnicity or school poverty. They also discuss these strategies' implications for schools and districts. [This report was published by the RAND Corporation (ED612136).]