Abstract
Abstract This article develops a survey design where the questionnaire is split into components and individuals are administered the varying subsets of the components. A multiple imputation method for analyzing data from this design is developed, in which the imputations are created by random draws from the posterior predictive distribution of the missing parts, given the observed parts by using Gibbs sampling under a general location scale model. Results from two simulation studies that investigate the properties of the inferences using this design are reported. In the first study several random split questionnaire designs are imposed on the complete data from an existing survey collected using a long questionnaire, and the corresponding data elements are extracted to form split data sets. Inferences obtained using the complete data and the split data are then compared. This comparison suggests that little is lost, at least in the example considered, by administering only parts of the questionnaire to ea...
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