In almost any research study,
there will be missing or incomplete data. Missing data can happen for a number
of reasons: participants fail to respond to questions, subjects withdraw or
quit studies before they are completed, and data entry errors.
The problem with missing data is
nearly all statistical techniques assume or require complete data. There can be
legitimately missing data; an example might be a survey in which a person is
asked if he or she is married, and if so how long. If you are not married, then
you would be correct in leaving the "how long" portion of the
question blank.
It is also important to realize
legitimately missing data can be meaningful. The missing data allows a validity
check and may inform the status of an individual. Osborn (2013) provides a
great example. In cleaning the data from an adolescent health risk survey, he
noticed some individuals indicated on one question they had never used illegal
drugs, but later in the survey when asked how many times they used marijuana,
indicated an answer greater than 0. Therefore, an answer they should have
skipped (or be missing), showed an unexpected number. The author suggests
several possible explanations, such as the subject was not paying attention and
answered in error. However, a more intriguing possibility is some subjects did
not view marijuana as an illegal drug, which is an interesting possibility that
could be examined in future research.
One way of dealing with
legitimately missing data is making the missing and present data two separate
groups. Using the marriage survey example, we could eliminate non-married
individuals from a specific analysis when looking at issues related to being
married vs. not married. So instead of asking the silly research question,
"How long, on average, do all people, even unmarried people, stay
married;" we can ask two more refined questions: "What are the
predictors of whether someone is currently married?" and "Of those
who are currently married, how long on average have they been married?"
Next time we will consider categories
of missing data. 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
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