Nonrandom missingness in categorical data: Strengths and limitations

There have recently been substantial developments in the analysis of incomp lete data. Modeling tools are now available for nonrandom missingness and t hese methods are finding their way into the broad statistical community. Th e computational and interpretational issues that surround such models are l ess well known. This article provides an exposition of several of these iss ues in a categorical data setting. It is argued that the use of contextual information can aid the modeler in discriminating among models that are ind istinguishable purely on statistical grounds.