Hi there,
I'm an undergrad Psychology student, I was taught in my statistics class that it is recommended to use pairwise deletion when dealing with missing data to not reduce your sample size and statistical power. I have been doing reading on Little's Missing Completely at Random analysis (MCAR) as well as imputation techniques like multiple imputation, maximum likelihood and full information maximum likelihood to deal with missing data instead of deletion.
My question is if a researcher were to have a significant p-value on the MCAR test and then use pairwise deletion would this mean that the sampling is not random, but instead based on whether a participant responds to a question or not. If so, does this then eliminate the generalizability of the research?
Dr. Wilhelm is completely correct. I would not go quite as far as he did on hypothesis tests. The important thing here is that pairwise deletion makes it less likely that you are sampling from the population of interest and if asked by some reviewer it's harder to say that you are talking about the population of interest instead of some other. This can defeat your argument for generalizability. Who suggested pairwise deletion? that I believe is from the SPSS literature. I would not suggest that as a good source. Best wishes, David Booth
Your best bet is to go with multiple imputation or full information maximum likelihood estimation which both only require the less restrictive assumption of MAR (both work well with MCAR data also). In addition, it is a good idea to identify as many auxiliary variables (variables related to/correlated with missingness and/or the DV) as possible and include those variables in your multiple imputation procedure or FIML estimation. That way, you may get closer to meeting or at least approximating the MAR condition, increase statistical power, and reduce bias due to missing data.
Jochen Wilhelm
David Eugene Booth Christian GeiserThank you all for your information, it is greatly appreciated :)
Jarryde
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