Mobile Web Surveys: a First Look at Measurement, Nonresponse, and Coverage Errors.

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dc.contributor.author Antoun, Christopher en_US
dc.date.accessioned 2016-01-13T18:05:05Z
dc.date.available NO_RESTRICTION en_US
dc.date.available 2016-01-13T18:05:05Z
dc.date.issued 2015 en_US
dc.date.submitted en_US
dc.identifier.uri http://hdl.handle.net/2027.42/116722
dc.description.abstract This dissertation focuses on the use of smartphones for Web surveys. The current state of knowledge about whether respondents are willing and able to accurately record their answers when using such devices is evolving, but far from complete. The primary purpose of my research is therefore to investigate the implications of this new mode for various sources of error using a Total Survey Error (TSE) perspective. Each chapter reports on a different aspect of a mode experiment that I designed to compare the effect of completion device (smartphone vs. computer) on survey errors. The experiment was carried out using the LISS panel (Longitudinal Internet Studies for the Social Sciences), a probability-based Web panel administered by CentERdata at Tilburg University in the Netherlands. The first analysis (Chapter 2) compares response quality in the two modes. When using smartphones, respondents in this study really were more mobile and more engaged with the other people and other tasks compared to when using computers. Despite this, response quality – conscientious responding and disclosure of sensitive information – was equivalent between the two modes of data collection. The second analysis (Chapter 3) investigates the causes of nonresponse in the mobile Web version of the experiment. I found that several social, psychological, attitudinal, and behavioral measures are associated with nonresponse. These include factors known to influence participation decisions in other survey modes such as personality traits, civic engagement, and attitudes about surveys as well as factors that may be specific to this mode, including smartphone use, social media use, and smartphone e-mail use. The third analysis (Chapter 4) estimates multiple sources of error simultaneously in the mobile Web version of the experiment. Errors are estimated as a mode effect against the conventional Web survey, which serves as the benchmark. I find few overall mode effects and no evidence whatsoever of measurement effects, but a significant impact of non-coverage bias for over one-third of the estimates. Collectively, these findings suggest that non-observation errors (i.e., coverage and nonresponse), not measurement errors, are the largest obstacle to the adoption of mobile Web surveys for population-based inference. en_US
dc.language.iso en_US en_US
dc.subject mobile Web survey en_US
dc.subject smartphone survey en_US
dc.subject survey methodology en_US
dc.subject measurement error en_US
dc.subject survey nonresponse en_US
dc.subject coverage error en_US
dc.title Mobile Web Surveys: a First Look at Measurement, Nonresponse, and Coverage Errors. en_US
dc.type Thesis en_US
dc.description.thesisdegreename PhD en_US
dc.description.thesisdegreediscipline Survey Methodology en_US
dc.description.thesisdegreegrantor University of Michigan, Horace H. Rackham School of Graduate Studies en_US
dc.contributor.committeemember Conrad, Frederick G en_US
dc.contributor.committeemember Klasnja, Predrag en_US
dc.contributor.committeemember Couper, Michael P en_US
dc.contributor.committeemember West, Brady T en_US
dc.subject.hlbsecondlevel Statistics and Numeric Data en_US
dc.subject.hlbtoplevel Social Sciences en_US
dc.description.bitstreamurl http://deepblue.lib.umich.edu/bitstream/2027.42/116722/1/antoun_1.pdf
dc.owningcollname Dissertations and Theses (Ph.D. and Master's)
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