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STATISTICAL ESTIMATION METHODS IN VOLUNTEER PANEL WEB SURVEYS

dc.contributor.advisorValliant, Richarden_US
dc.contributor.authorLee, Sungheeen_US
dc.date.accessioned2005-02-02T06:27:06Z
dc.date.available2005-02-02T06:27:06Z
dc.date.issued2004-11-17en_US
dc.identifier.urihttp://hdl.handle.net/1903/2003
dc.description.abstractData collected through Web surveys, in general, do not adopt traditional probability-based sample designs. Therefore, the inferential techniques used for probability samples may not be guaranteed to be correct for Web surveys without adjustment, and estimates from these surveys are likely to be biased. However, research on the statistical aspect of Web surveys is lacking relative to other aspects of Web surveys. Propensity score adjustment (PSA) has been suggested as an alternative for statistically surmounting inherent problems, namely nonrandomized sample selection, in volunteer Web surveys. However, there has been a minimal amount of evidence for its applicability and performance, and the implications are not conclusive. Moreover, PSA does not take into account problems occurring from uncertain coverage of sampling frames in volunteer panel Web surveys. This study attempted to develop alternative statistical estimation methods for volunteer Web surveys and evaluate their effectiveness in adjusting biases arising from nonrandomized selection and unequal coverage in volunteer Web surveys. Specifically, the proposed adjustment used a two-step approach. First, PSA was utilized as a method to correct for nonrandomized sample selection, and secondly calibration adjustment was used for uncertain coverage of the sampling frames. The investigation found that the proposed estimation methods showed a potential for reducing the selection and coverage bias in estimates from volunteer panel Web surveys. The combined two-step adjustment not only reduced bias but also mean square errors to a greater degree than each individual adjustment. While the findings from this study may shed some light on Web survey data utilization, there are additional areas to be considered and explored. First, the proposed adjustment decreased bias but did not completely remove it. The adjusted estimates showed a larger variability than the unadjusted ones. The adjusted estimator was no longer in the linear form, but an appropriate variance estimator has not been developed yet. Finally, naively applying the variance estimator for linear statistics highly overestimated the variance, resulting in understating the efficiency of the survey estimates.en_US
dc.format.extent4514439 bytes
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.titleSTATISTICAL ESTIMATION METHODS IN VOLUNTEER PANEL WEB SURVEYSen_US
dc.typeDissertationen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.contributor.departmentSurvey Methodologyen_US
dc.subject.pqcontrolledStatisticsen_US
dc.subject.pqcontrolledSociology, Theory and Methodsen_US
dc.subject.pquncontrolledWeb Surveyen_US
dc.subject.pquncontrolledPropensity Score Adjustmenten_US
dc.subject.pquncontrolledCalibrationen_US
dc.subject.pquncontrolledPost Survey Adjustmenten_US


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