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American Journal of Epidemiology Advance Access originally published online on November 12, 2007
American Journal of Epidemiology 2008 167(3):369-374; doi:10.1093/aje/kwm297
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American Journal of Epidemiology © The Author 2007. Published by the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oxfordjournals.org.

PRACTICE OF EPIDEMIOLOGY

Handling Nonresponse in Surveys: Analytic Corrections Compared with Converting Nonresponders

Paul Jenkins1, Giulia Earle-Richardson2, Patrick Burdick1 and John May2

1 Bassett Research Institute, Cooperstown, NY
2 New York Center for Agricultural Medicine and Health, Cooperstown, NY

Correspondence to Dr. Paul Jenkins, MIBH Research Institute, One Atwell Road, Cooperstown, NY 13326 (e-mail: Paul.jenkins{at}bassett.org).

Received for publication March 22, 2007. Accepted for publication September 18, 2007.

A large health survey was combined with a simulation study to contrast the reduction in bias achieved by double sampling versus two weighting methods based on propensity scores. The survey used a census of one New York county and double sampling in six others. Propensity scores were modeled as a logistic function of demographic variables and were used in conjunction with a random uniform variate to simulate response in the census. These data were used to estimate the prevalence of chronic disease in a population whose parameters were defined as values from the census. Significant (p < 0.0001) predictors in the logistic function included multiple (vs. single) occupancy (odds ratio (OR) = 1.3), bank card ownership (OR = 2.1), gender (OR = 1.5), home ownership (OR = 1.3), head of household's age (OR = 1.4), and income >$18,000 (OR = 0.8). The model likelihood ratio chi-square was significant (p < 0.0001), with the area under the receiver operating characteristic curve = 0.59. Double-sampling estimates were marginally closer to population values than those from either weighting method. However, the variance was also greater (p < 0.01). The reduction in bias for point estimation from double sampling may be more than offset by the increased variance associated with this method.

bias (epidemiology); censuses; data collection; sampling studies; statistics


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