Missing data is a regular issue that
researchers and practitioners
must consider for treatment. Commonly, cases for which data is missing
are excluded from inclusion in larger data sets. However, this is
not the only option and could artificially alter the sample. Other
options are available for imputing missing data. Expanding on work
previously reported, a method is presented here that not only preserves
all observed data but also is shown to function for smaller data sets.
As an example of the process, four ACS Exams are used as prototypes
with a discussion on an expected noise level of any imputed sample.