Enhancing the Understanding of the Relationship between Social Integration and Nonresponse in Household Surveys

dc.contributor.advisorPresser, Stanleyen_US
dc.contributor.authorAmaya, Ashley Elaineen_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.accessioned2016-02-06T06:43:58Z
dc.date.available2016-02-06T06:43:58Z
dc.date.issued2015en_US
dc.description.abstractNonresponse and nonresponse bias remain fundamental concerns for survey researchers as understanding them is critical to producing accurate statistics. This dissertation tests the relationship between social integration, nonresponse, and nonresponse bias. Using the rich frame information available on the American Time Use Survey (ATUS) and the Survey of Health, Ageing, and Retirement in Europe (SHARE) Wave II, structural equation models were employed to create latent indicators of social integration. The resulting variables were used to predict nonresponse and its components (e.g., noncontact). In both surveys, social integration was significantly predictive of nonresponse (regardless of type of nonresponse) with integrated individuals more likely to respond. However, the relationship was driven by different components of integration across the two surveys. Full sample estimates were compared to respondent estimates on a series of 40 dichotomous and categorical variables to test the hypothesis that variables measuring social activities and roles would suffer from nonresponse bias. The impact of nonresponse on multivariate models predicting social outcomes was also evaluated. Nearly all of the 40 assessed variables suffered from significant nonresponse bias resulting in the overestimation of social activity and role participation. In general, civic and political variables suffered from higher levels of bias, but the differences were not significant. Multivariate models were not exempt; beta coefficients were frequently biased. Although, the direction was inconsistent and often small. Finally, an indicator of social integration was added to the weighting methodology with the goal of eliminating the observed nonresponse bias. While the addition significantly reduced the bias in most instances compared to both the base- and traditionally-weighted estimates, the improvements were small and did little to eliminate the bias.en_US
dc.identifierhttps://doi.org/10.13016/M29D90
dc.identifier.urihttp://hdl.handle.net/1903/17305
dc.language.isoenen_US
dc.subject.pqcontrolledSocial researchen_US
dc.subject.pquncontrolledAmerican Time Use Surveyen_US
dc.subject.pquncontrolledNonresponseen_US
dc.subject.pquncontrolledNonresponse biasen_US
dc.subject.pquncontrolledSocial integrationen_US
dc.subject.pquncontrolledSurvey of Healthen_US
dc.subject.pquncontrolledAgeingen_US
dc.subject.pquncontrolledRetirement in Europeen_US
dc.titleEnhancing the Understanding of the Relationship between Social Integration and Nonresponse in Household Surveysen_US
dc.typeDissertationen_US

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