It would be useful to have a general, easily computed diagnostic that characterizes data sets with respect to their potential for sensitivity to nonignorability. The investigators have developed a diagnostic that measures the effect of small perturbations from ignorability on coefficient estimates in the univariate linear model with missing observations.They will extend their analysis in a number of directions: i) They will develop a general class of diagnostics for Bayes and direct- likelihood inferences, and demonstrate its application to a number of important special cases. ii) They will develop an analogous theory for sensitivity to nonignorability in frequentist estimation and testing. iii) They will develop a general form of the diagnostic for the coarse-date model, a generalization of missing data that includes censoring and rounding as special cases. iv) They will analyze a number of real- world data sets that represent important cases where nonignorability is of interest, including dropout in longitudinal data, censored survival data, and cross-over in clinical trials.