Linear model analysis of categorical data with incomplete response vectors.

The general linear model approach to the analysis of categorical data described by Grizzle et al. [1969] is extended to situations where: (1) missing data for certain individuals arise at random as a result of non-response or deleted incorrect response; (2) supplemental samples pertaining to various subsets of variables have been obtained due to cost considerations and/or special interest in these variables. The problems discussed are distinct from those involving 'incomplete contingency tables' containing a priori empty cells. The extension is presented through a series of examples which show how the approach can be used to handle a wide variety of non-standard data configurations. Applications to categorical data mixed models and split plot designs are emphasized.

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