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

PSC In The News

RSS Feed icon

Smock cited in amicus brief for Supreme Court case on citizenship rights for foreign-born children of unwed parents

Levy, Buchmueller and colleagues examine Medicaid expansion's impact on ER visits

ISR data show large partisan gap in consumer expectations for economy

More News

Highlights

MiCDA Research Fellowship - applications due July 21, 2017

U-M awarded $58 million to develop ideas for preventing and treating health problems

Bailey, Eisenberg , and Fomby promoted at PSC

Former PSC trainee Eric Chyn wins PAA's Dorothy S. Thomas Award for best paper

More Highlights

A new stopping rule for surveys

Publication Abstract

Wagner, J., and Trivellore Raghunathan. 2010. "A new stopping rule for surveys." Statistics in Medicine, 29(9): 1014-1024.

Non-response is a problem for most surveys. In the sample design, non-response is often dealt with by setting a target response rate and inflating the sample size so that the desired number of interviews is reached. The decision to stop data collection is based largely on meeting the target response rate. A recent article by Rao, Glickman, and Glynn (RGG) suggests rules for stopping that are based on the survey data collected for the current set of respondents. Two of their rules compare estimates from fully imputed data where the imputations are based on a subset of early responders to fully imputed data where the imputations are based on the combined set of early and late responders. If these two estimates are different, then late responders are changing the estimate of interest. The present article develops a new rule for when to stop collecting data in a sample survey. The rule attempts to use complete interview data as well as covariates available on non-responders to determine when the probability that collecting additional data will change the survey estimate is sufficiently low to justify stopping data collection. The rule is compared with that of RGG using simulations and then is implemented using data from a real survey. Copyright (C) 2010 John Wiley & Sons, Ltd.

DOI:10.1002/sim.3834 (Full Text)

Browse | Search : All Pubs | Next