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Bayesian analysis of binary survey data
Author(s) -
Stroud T.W.F.
Publication year - 1994
Publication title -
canadian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.804
H-Index - 51
eISSN - 1708-945X
pISSN - 0319-5724
DOI - 10.2307/3315826.n2
Subject(s) - cluster sampling , logistic regression , statistics , bayesian probability , mathematics , stratified sampling , hyperparameter , sampling (signal processing) , bayes' theorem , simple random sample , logit , computer science , demography , algorithm , sociology , population , filter (signal processing) , computer vision
First a comprehensive treatment of the hierarchical‐conjugate Bayesian predictive approach to binary survey data is presented, encompassing simple random, stratified, cluster, and two‐stage sampling, as well as two‐stage sampling within strata. For the case of two‐stage sampling within strata when there is more than one variable of stratification, analysis using an unsaturated logit linear model on the prior means is proposed. This allows there to be cells containing no sampled clusters. Formulas for posterior predictive means, variances, and covariances of numbers of successes in unsampled portions of clusters are presented in terms of posterior expectations of certain functions of hyperparameters; these may be evaluated by existing methods. The technique is illustrated using a small subset of Canada Youth & AIDS Study data. A sample of students within each of various selected school boards was chosen and interviewed via questionnaire. The boards were stratified/poststratified in two dimensions, but some of the resulting cells contained no data. The additive logit linear model on the prior means produced estimates and posterior variances for boards in all cells. Data showed the additive model to be plausible.