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

PSC In The News

RSS Feed icon

H. Luke Shaefer and colleagues argue for a universal child allowance

Hindustan Times points out high value of H-1B visas for US innovation, welfare, and tech firm profits

Novak, Geronimus, Martinez-Cardoso: Threat of deportation harmful to immigrants' health

More News

Highlights

Heather Ann Thompson wins Pulitzer Prize for book on Attica uprising

Lam explores dimensions of the projected 4 billion increase in world population before 2100

ISR's Nick Prieur wins UMOR award for exceptional contribution to U-M's research mission

How effectively can these nations handle outside investments in health R&D?

More Highlights

Next Brown Bag

Mon, April 10, 2017, noon:
Elizabeth Bruch

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