Monroe G. Sirken Award Lecture

Barry Graubard Chair
National Cancer Institute
J. Michael Brick Organizer
Wednesday, Aug 9: 10:30 AM - 12:20 PM
Invited Paper Session 
Metro Toronto Convention Centre 
Room: CC-714B 



Main Sponsor

Sirken Award


Unifying Design-Based and Model-Based Sampling Inference

A well-known rule in practical survey research is to include weights when estimating a population average but not to use weights when fitting a regression model--as long as the regression includes as predictors all the information that went into the sampling weights. But it is not clear how to apply this advice when fitting regressions that include only some of the weighting information, nor does it tell us what to do when analyzing already-collected surveys where the weighting procedure has not been clearly explained or where the weights depend in part on information that is not available in the data. It is also not clear how one is supposed to account for clustering in such analyses. We propose a quasi-Bayesian approach using a joint regression of the outcome and the logarithm of sampling weight, poststratifying to obtain inference for the quantity of interest or regression in the population, thus using design information within a model-based context. 


Andrew Gelman, Columbia University