Home > Publications . Search All . Browse All . Country . Browse PSC Pubs . PSC Report Series

PSC In The News

RSS Feed icon

Groves keynote speaker at MIDAS symposium, Nov 15-16: "Big Data: Advancing Science, Changing the World"

Shaefer says drop child tax credit in favor of universal, direct investment in American children

Buchmueller breaks down partisan views on Obamacare

More News


Gonzalez, Alter, and Dinov win NSF "Big Data Spokes" award for neuroscience network

Post-doc Melanie Wasserman wins dissertation award from Upjohn Institute

ISR kicks off DE&I initiative with lunchtime presentation: Oct 13, noon, 1430 ISR Thompson

U-M ranked #4 in USN&WR's top public universities

More Highlights

Next Brown Bag

Mon, Oct 24 at noon:
Academic innovation & the global public research university, James Hilton

A comparison of variance estimators for poststratification to estimated control totals

Archived Abstract of Former PSC Researcher

Dever, J.A., and Richard L. Valliant. 2010. "A comparison of variance estimators for poststratification to estimated control totals." Survey Methodology, 36(1): 45-56.

Calibration techniques, such as poststratification, use auxiliary information to improve the efficiency of survey estimates. The control totals, to which sample weights are poststratified (or calibrated), are assumed to be population values. Often, however, the controls are estimated from other surveys. Many researchers apply traditional poststratification variance estimators to situations where the control totals are estimated, thus assuming that any additional sampling variance associated with these controls is negligible. The goal of the research presented here is to evaluate variance estimators for stratified, multi-stage designs under estimated-control (EC) poststratification using design-unbiased controls. We compare the theoretical and empirical properties of linearization and jackknife variance estimators for a poststratified estimator of a population total. Illustrations are given of the effects on variances from different levels of precision in the estimated controls. Our research suggests (i) traditional variance estimators can seriously underestimate the theoretical variance, and (ii) two EC poststratification variance estimators can mitigate the negative bias.

Country of focus: United States of America.

Browse | Search : All Pubs | Next