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Model‐based inference for categorical survey data subject to non‐ignorable non‐response
Author(s) -
Forster Jonathan J.,
Smith Peter W. F.
Publication year - 1998
Publication title -
journal of the royal statistical society: series b (statistical methodology)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.523
H-Index - 137
eISSN - 1467-9868
pISSN - 1369-7412
DOI - 10.1111/1467-9868.00108
Subject(s) - categorical variable , polling , econometrics , computer science , inference , covariate , bayesian probability , bayesian inference , statistics , mathematics , artificial intelligence , machine learning , operating system
We consider non‐response models for a single categorical response with categorical covariates whose values are always observed. We present Bayesian methods for ignorable models and a particular non‐ignorable model, and we argue that standard methods of model comparison are inappropriate for comparing ignorable and non‐ignorable models. Uncertainty about ignorability of non‐response is incorporated by introducing parameters describing the extent of non‐ignorability into a pattern mixture specification and integrating over the prior uncertainty associated with these parameters. Our approach is illustrated using polling data from the 1992 British general election panel survey. We suggest sample size adjustments for surveys when non‐ignorable non‐response is expected.