To Model or Not to Model? Competing Modes of Inference for Finite Population Sampling
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Abstract:
Finite population sampling is perhaps the only area of statistics where the primary mode of analysis is based on the randomization distribution, rather than on statistical models for the measured variables. This article reviews the debate between design and model-based inference. The basic features of the two approaches are illustrated using the case of inference about the mean from stratified random samples. Strengths and weakness of design-based and model-based inference for surveys are discussed. It is suggested that models that take into account the sample design and make weak parametric assumptions can produce reliable and efficient inferences in surveys settings. These ideas are illustrated using the problem of inference from unequal probability samples. A model-based regression analysis that leads to a combination of design-based and model-based weighting is described.
Subject Area:
Design of Experiments and Sample Surveys, Institutional and Historical, Statistical Models
Suggested Citation:
Rod Little, "To Model or Not to Model? Competing Modes of Inference for Finite Population Sampling" (November 2003). The University of Michigan Department of Biostatistics Working Paper Series. Working Paper 4.
http://www.bepress.com/umichbiostat/paper4
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July 15, 2003