Missing Data

Today we will take a look at methods of dealing with missing data. SPSS offers pairwise exclusion, which means only those cases with complete data are included in the analysis. It the missing data are few and 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 that 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.

Osborn, J. W. (2013). Best practices in data cleaning. DC: Sage.

Osborn, J. W. (2000). Prediction in multiple regression. Practical Assessment, Research, & Evaluation, 7(2).

Next time we will consider missing data as a variable and best practices. 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