Andreas Wolf sent me the following reference, which may of interest to
others on the list.....
Collins, L.M., Schafer, J.L. & Kam, C.M. (2001). A comparison of inclusive
and restrictive strategies in modern missing data procedures. Psychological
Methods, 6 (4), 330-351.
Abstract:
Two classes of modern missing data procedures, maximum likelihood (ML) and
multiple imputation (MI), tend to yield similar results when implemented in
comparable ways. In either approach, it is possible to include auxiliary
variables solely for the purpose of improving the missing data procedure. A
simulation was presented to assess the potential costs and benefits of a
restrictive strategy, which makes minimal use of auxiliary variables,
versus an inclusive strategy, which makes liberal use of such variables.
The simulation showed that the inclusive strategy is to be greatly
preferred. With an inclusive strategy not only is there a reduced chance of
inadvertently omitting an important cause of missingness, there is also the
possibility of noticeable gains in terms of increased efficiency and
reduced bias, with only minor costs. As implemented in currently available
software, the ML approach tends to encourage the use of a restrictive
strategy, whereas the MI approach makes it relatively simple to use an
inclusive strategy
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