  Methods for addressing missing data have become much more accessible to
applied researchers. However, little guidance exists to help researchers
systematically identify plausible missing data mechanisms in order to ensure
that these methods are appropriately applied. Two considerations motivate the
present study. First, psychological research is typically characterized by a
large number of potential response variables that may be observed across
multiple waves of data collection. This situation makes it more challenging to
identify plausible missing data mechanisms than is the case in other fields
such as biostatistics where a small number of dependent variables is typically
of primary interest and the main predictor of interest is statistically
independent of other covariates. Second, there is growing recognition of the
importance of systematic approaches to sensitivity analyses for treatment of
missing data in psychological science. We develop and apply a systematic
approach for reducing a large number of observed patterns and demonstrate how
these can be used to explore potential missing data mechanisms within
multivariate contexts. A large scale simulation study is used to guide
suggestions for which approaches are likely to be most accurate as a function
of sample size, number of factors, number of indicators per factor, and
proportion of missing data. Three applications of this approach to data
examples suggest that the method appears useful in practice.
