There are many challenges that occur related to missing values in a variety of single point and longitudinal studies and surveys. This can include missing values in which the subject elected to include a missing value. But this issue can occur in studies that are more exploratory in nature and data is "censored" during data lock to prevent identification of individuals. When this may occur, potential ways to address this issue, and most importantly how to alert readers to the obscuring of data is a critical step. A public domain example that examines this issues and explores the potential ramifications of this censoring will be discussed and explored.