Premium
A new stopping rule for surveys
Author(s) -
Wagner James,
Raghunathan Trivellore E.
Publication year - 2010
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.3834
Subject(s) - computer science , data collection , survey data collection , data set , sample (material) , sample size determination , statistics , covariate , decision rule , set (abstract data type) , stopping rule , data mining , econometrics , survey sampling , mathematics , artificial intelligence , machine learning , medicine , population , chemistry , environmental health , chromatography , programming language , mathematical optimization
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 © 2010 John Wiley & Sons, Ltd.