Adjustments for Nonresponse, Sample Quality Indicators, and Nonresponse Error in a Total Survey Error Context

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dc.contributor.advisorTourangeau, Rogeren_US
dc.contributor.authorYe, Congen_US
dc.contributor.departmentSurvey Methodologyen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.date.accessioned2013-04-04T05:46:32Z
dc.date.available2013-04-04T05:46:32Z
dc.date.issued2012en_US
dc.description.abstractThe decline in response rates in surveys of the general population is regarded by many researchers as one of the greatest threats to contemporary surveys. Much research has focused on the consequences of nonresponse. However, because the true values for the non-respondents are rarely known, it is difficult to estimate the magnitude of nonresponse bias or to develop effective methods for predicting and adjusting for nonresponse bias. This research uses two datasets that have records on each person in the frame to evaluate the effectiveness of adjustment methods aiming to correct nonresponse bias, to study indicators of sample quality, and to examine the relative magnitude of nonresponse bias under different modes. The results suggest that both response propensity weighting and GREG weighting, are not effective in reducing nonresponse bias present in the study data. There are some reductions in error, but the reductions are limited. The comparison between response propensity weighting and GREG weighting shows that with the same set of auxiliary variables, the choice between response propensity weighting and GREG weighting makes little difference. The evaluation of the R-indicators and the penalized R-indicators using the study datasets and from a simulation study suggests that the penalized R-indicators perform better than the R-indicators in terms of assessing sample quality. The penalized R-indicator shows a pattern that has a better match to the pattern for the estimated biases than the R-indicator does. Finally, the comparison of nonresponse bias to other types of errors finds that nonresponse bias in these two data sets may be larger than sampling error and coverage bias, but measurement bias can be bigger in turn than nonresponse bias, at least for sensitive questions. And postsurvey adjustments do not result in substantial reduction in the total survey error. We reach the conclusion that 1) efforts put into dealing with nonresponse bias are warranted; 2) the effectiveness of weighting adjustments for nonresponse depends on the availability and quality of the auxiliary variables, and 3) the penalized R-indicator may be more helpful in monitoring the quality of the survey than the R-indicator.en_US
dc.identifier.urihttp://hdl.handle.net/1903/13847
dc.subject.pqcontrolledStatisticsen_US
dc.subject.pqcontrolledSociologyen_US
dc.subject.pqcontrolledSocial psychologyen_US
dc.subject.pquncontrollednonresponse biasen_US
dc.subject.pquncontrollednonresponse weighting adjustmentsen_US
dc.subject.pquncontrolledpenalized R-indicatoren_US
dc.subject.pquncontrolledsurvey modesen_US
dc.subject.pquncontrolledsurvey nonresponseen_US
dc.subject.pquncontrolledtotal survey erroren_US
dc.titleAdjustments for Nonresponse, Sample Quality Indicators, and Nonresponse Error in a Total Survey Error Contexten_US
dc.typeDissertationen_US

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