Joint Program in Survey Methodology Theses and Dissertations
Permanent URI for this collectionhttp://hdl.handle.net/1903/2782
Browse
2 results
Search Results
Item Improving External Validity of Epidemiologic Analyses by Incorporating Data from Population-Based Surveys(2020) Wang, Lingxiao; Li, Yan; Survey Methodology; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Many epidemiologic studies forgo probability sampling and turn to volunteer-based samples because of cost, confidentiality, response burden, and invasiveness of biological samples. However, the volunteers may not represent the underlying target population mainly due to self-selection bias. Therefore, standard epidemiologic analyses may not be generalizable to the target population, which is called lack of “external validity.” In survey research, propensity score (PS)-based approaches have been developed to improve representativeness of the nonprobability samples by using population-based surveys as references. These approaches create a set of “pseudo-weights” to weight the nonprobability sample up to the target population. There are two main types of PS-based approaches: (1) PS-based weighting methods using PSs to estimate participation rates of the nonprobability sample; for example, the inverse of PS weighting (IPSW); (2) PS-based matching methods using PSs to measure similarity between the units in the nonprobability sample and the reference survey sample, such as PS adjustment by subclassification (PSAS). Although the PS-based weighting methods reduce the bias, they are sensitive to propensity model misspecification and can be inefficient. The PS-based matching methods are more robust to the propensity model misspecification and can avoid extreme weights. However, matching methods such as PSAS are less effective at bias reduction. This dissertation proposes a novel PS-based matching method, named the kernel weighting (KW) approach, to improve the external validity of epidemiologic analyses that gain a better bias–variance tradeoff. A unifying framework is established for PS-based methods to provide three advances. First, the KW method is proved to provide consistent estimates, yet generally has smaller mean-square error than the IPSW. Second, the framework reveals a fundamental strong exchangeability assumption (SEA) underlying the existing PS-based matching methods that has previously been unknown. The SEA is relaxed to a weak exchangeability assumption that is more realistic for data analysis. Third, survey weights are scaled in propensity estimation to reduce the variance of the estimated PS and improve efficiency of all PS-based methods under the framework. The performance of the proposed PS-based methods is evaluated for estimating prevalence of diseases and associations between risk factors and disease in the finite population.Item The Use of Responsive Split Questionnaires in a Panel Survey(2012) Gonzalez, Jeffrey Mark; Valliant, Richard; Survey Methodology; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Lengthy surveys may be associated with high respondent burden, low data quality, and high unit nonresponse. To address these concerns, survey designers may reduce the length of a survey by eliminating questions from the original questionnaire, but this means that some information would never get collected. An alternative may be to divide a lengthy questionnaire into subsets of survey items and then administer each subset to distinct subsamples of the full sample. This is referred to as a split questionnaire design and has the benefit of collecting all of the original survey information. We identify a significant deficiency in the current set of split questionnaire methods, namely, the incomplete use of prior information about the sample unit in the design. In most contemporary applications of split questionnaires, generally only characteristics of the survey items (e.g., content, cognitive burden) are used to inform the design; however, if joint consideration is given to characteristics on the survey items as well as the sample unit when designing a split questionnaire, then there may be the potential to improve the split questionnaire's utility. In this dissertation, we explore the extent to which, if any, jointly considering both types of information at the design stage will yield more efficient split questionnaires. We propose various methods for incorporating prior information about the sample unit into the split questionnaire using features of responsive design. We highlight how this specific application of a responsive split questionnaire can be used to address the concerns present in a major federal survey. Finally, we draw from the literature pertaining to survey design, experimental design, and epidemiology to develop and implement a framework for evaluating the proposed new elements of our split questionnaire design.