Theses and Dissertations from UMD
Permanent URI for this communityhttp://hdl.handle.net/1903/2
New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a give thesis/dissertation in DRUM
More information is available at Theses and Dissertations at University of Maryland Libraries.
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Item Statistical Inference Using Data From Multiple Files Combined Through Record Linkage(2018) HAN, YING; Lahiri, Partha; Mathematical Statistics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Record linkage methods help us combine multiple data sets from different sources when a single data set with all necessary information is unavailable or when data collection on additional variables is time consuming and extremely costly. Linkage errors are inevitable in the linked data set because of the unavailability of an error-free and unique identifier and because of possible errors in measuring or recording. It has been realized that even a small amount of linkage errors can lead to substantial bias and increase variability in estimating the parameters of a statistical model. The importance of incorporating uncertainty of the record linkage process into the statistical analysis step cannot be overemphasized. The current research is mainly focused on the regression analysis of the linked data. The record linkage and statistical analysis processes are treated as two separate steps. Due to the limited information about the record linkage process, simplifying assumptions on the linkage mechanism have to be made. In reality, however, these assumptions may be violated. Also, most of the existing linkage error models are built on the linked data set, which only contains records for the designated links. Information about linkage errors carried by the designated non-links is missing. In the dissertation, we provide general methodologies for both regression analysis and small area estimation using data from multiple files. A general integrated model is proposed to combine the record linkage and statistical analysis processes. The proposed linkage error models are built directly on the data values from the original sources, and based on the actual record linkage method that is used. We have adapted the jackknife methods to estimate bias, variance, and mean squared error of our proposed estimators. To illustrate the general methodology, we give one example of estimating the regression coefficients in the linear and logistic regression models, and another example of estimating small area mean under the nested-error linear regression model. In order to reduce the computational burden, simplified version of the proposed estimators, jackknife methods, and numerical algorithms are given. A Monte Carlo simulation study is devised to evaluate the performance of the proposed estimators and to investigate the difference between the standard and simplified jackknife methods.Item Respondent Consent to Use Administrative Data(2012) Fulton, Jenna Anne; Presser, Stanley; Survey Methodology; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Surveys increasingly request respondents' consent to link survey responses with administrative records. Such linked data can enhance the utility of both the survey and administrative data, yet in most cases, this linkage is contingent upon respondents' consent. With evidence of declining consent rates, there is a growing need to understand factors associated with consent to record linkage. This dissertation presents the results of three research studies that investigate factors associated with consenting. In the first study, we draw upon surveys conducted in the U.S. with consent requests to describe characteristics of surveys containing such requests, examine trends in consent rates over time, and evaluate the effects of several characteristics of the survey and consent request on consent rates. The results of this study suggest that consent rates are declining over time, and that some characteristics of the survey and consent request are associated with variations in consent rates, including survey mode, administrative record topic, personal identifier requested, and whether the consent request takes an explicit or opt-out approach. In the second study, we administered a telephone survey to examine the effect of administrative record topic on consent rates using experimental methods, and through non-experimental methods, investigated the influence of respondents' privacy, confidentiality, and trust attitudes and consent request salience on consent rates. The results of this study indicate that respondents' confidentiality attitudes are related to their consent decision; the other factors examined appear to have less of an impact on consent rates in this survey. The final study used data from the 2009 National Immunization Survey (NIS) to assess the effects of interviewers and interviewer characteristics on respondents' willingness to consent to vaccination provider contact. The results of this study suggest that interviewers vary in their ability to obtain respondents' consent, and that some interviewer characteristics are related to consent rates, including gender and amount of previous experience on the NIS.