The problem of disguised missing data

Abstract

Missing data is a well-recognized problem in large datasets, widely discussed in the statistics and data analysis literature. Many programming environments provide explicit codes for missing data, but these are not standardized and are not always used. This lack of standardization is one of the leading causes of the subtle problem of disguised missing data , in which unknown, inapplicable, or otherwise nonspecified responses are encoded as valid data values. Following a brief overview of the problem of explicitly coded missing data, this paper discusses sources, consequences, and detection of disguised missing data, including two real-world examples. As the first of these examples illustrates, the consequences of disguised missing data can be quite serious. The key to its detection lies in first, recognizing disguised missing data as a possibility and second, finding a sufficiently informative view of the data to reveal its presence.