Missing Data as a Variable/ Best Practices

You may wish to examine missing data as an outcome itself, as there may be information in the missing-ness. The act of failing to respond vs. responding might be of interest. This can be examined through a "dummy variable," a new variable you create representing whether a person has missing data or not on a particular variable. You can then do some analyses to see if there are any relationships that develop.

Osborne (2013) provides some best practices in dealing with missing data, which are great to remember.

•            First, do no harm; be careful in your methodology to minimize missing data.

•            Be transparent. Report any incidence of missing data (rates by variable, and reason for missing data if known). This can be important information for readers.

•            Explicitly discuss whether data are missing at random (i.e., if there are differences between individuals with complete and incomplete data).

•            Discuss how you, as the researcher, dealt with the issue of incomplete data. 

Next time we will consider extreme scores. Do you have an issue or a question that you would like me to discuss in a future post? Would you like to be a guest writer? Send me your ideas! leann.stadtlander@waldenu.edu