The role of the sample selection mechanism in a model-based approach to finite population inference is examined. When the data analyst has only partial information on the sample design then a design which is ignorable when known fully may become informative. Conditions under which partially known designs can be ignored are established and examined for some standard designs. The results are illustrated by an example used by Scott (1977).
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SUMMARYThe role of the sample selection mechanism in a model-based approach to finite population inference is examined. When the data analyst has only partial information on the sample design then a design which is ignorable when known fully may become informative. Conditions under which partially known designs can be ignored are established and examined for some standard designs. The results are illustrated by an example used by Scott (1977).
Some key words: Bayesian predictive inference; Face-value likelihood; Finite population; Model-based inference; Partial design information; Regression through the origin; Secondary analysis; Selection mechanism.This content downloaded from 188.72.96.55 on Tue, 17 Jun 2014 05:30:03 AM All use subject to JSTOR Terms and Conditions
A general framework is given for examining the role of mechanisms for treatment assignment and unit selection in experiments, surveys and observational studies. Conditions are established under which these mechanisms can be ignored for model-based inference. Examples are presented to show how inference can incorporate the mechanisms when the conditions do not hold.
Cochran's rule for the minimum sample size to ensure adequate coverage of nominal 95% con®dence intervals is derived by using the Edgeworth expansion for the distribution function of the standardized sample mean. The rule is extended for con®dence intervals based on the Studentized sample mean. The performance of the rule and Edgeworth approximations for smaller sample sizes are examined by simulation.
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