As part of the ACS Examinations Institute (ACS-EI) national norming process, student performance data sets are collected from professors at colleges and universities from around the United States. Because the data sets are collected on a volunteer basis, the ACS-EI often receives data sets with only students' total scores and without the students' responses to individual exam questions. Nonetheless, several national norming statistics require students' item responses. This data return leads to missing data and potentially biased results when inferences are made based on that data set. This work uses student performance data sets from ACS-EI to consider how methods for replacing missing data, such as hot-deck imputation and simulating data, affect the nature of the analysis of quantitative data.