How can you deal with missing data?
SPSS offers pairwise deletion, which means only those cases with complete data
are included in the analysis. If you have few missing data and they are a
result of randomness, then such a plan may be acceptable. However, if they are
not randomly missing, you could introduce biases.
A second commonly used method is
substituting the overall sample's mean for the missing data. The logic of this
is that in absence of any other information, the sample's mean is the best
representation of an individual's score. If only a few scores are missing, then
this may be an acceptable alternative. However, keep in mind the more scores
that are replaced, the more you are biasing the sample to the mean.
A third alternative is given by
Osborne (2000, 2013) in which a prediction equation is developed through
multiple regression. If you have quite a few missing scores, you may want to
explore this alternative.
Next time we will consider best
practices and 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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