Joint Program in Survey Methodology Theses and Dissertations

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    Optimizing stratified sampling allocations to account for heteroscedasticity and nonresponse
    (2023) Mendelson, Jonathan; Elliott, Michael R; Lahiri, Partha; Survey Methodology; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Neyman's seminal paper in 1934 and subsequent developments of the next two decades transformed the practice of survey sampling and continue to provide the underpinnings of today's probability samples, including at the design stage. Although hugely useful, the assumptions underlying classic theory on optimal allocation, such as complete response and exact knowledge of strata variances, are not always met, nor is the design-based approach the only way to identify good sample allocations. This thesis develops new ways to allocate samples for stratified random sampling (STSRS) designs. In Papers 1 and 2, I provide a Bayesian approach for optimal STSRS allocation for estimating the finite population mean via a univariate regression model with heteroscedastic errors. I use Bayesian decision theory on optimal experimental design, which accommodates uncertainty in design parameters. By allowing for heteroscedasticity, I aim for improved realism in some establishment contexts, compared with some earlier Bayesian sample design work. Paper 1 assumes that the level of heteroscedasticity is known, which facilitates analytical results. Paper 2 relaxes this assumption, which results in an analytically intractable problem. Thus, I develop a computational approach that uses Monte Carlo sampling to estimate the loss for a given allocation, in conjunction with a stochastic optimization algorithm that accommodates noisy loss functions. In simulation, the proposed approaches performed as well or better than the design-based and model-assisted strategies considered, while having clearer theoretical justification. Paper 3 changes focus toward addressing how to account for nonresponse when designing samples. Existing theory on optimal STSRS allocation generally assumes complete response. A common practice is to allocate sample under complete response, then to inflate the sample sizes by the inverse of the anticipated response rates. I show that this practice overcorrects for nonresponse, leading to excessive costs per effective interview. I extend the existing design-based framework for STSRS allocation to accommodate scenarios with incomplete response. I provide theoretical comparisons between my allocation and common alternatives, which illustrate how response rates, population characteristics, and cost structure can affect the methods' relative efficiency. In an application to a self-administered survey of military personnel, the proposed allocation resulted in a 25% increase in effective sample size compared with common alternatives.
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    (2020) Coffey, Stephanie Michelle; Elliott, Michael R; Survey Methodology; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Adaptive and responsive survey designs rely on estimates of survey data collection parameters (SDCPs), such as response propensity, to make intervention decisions during data collection. These interventions are made with some data collection goal in mind, such as maximizing data quality for a fixed cost or minimizing costs for a fixed measure of data quality. Data quality may be defined by response rate, sample representativeness, or error in survey estimates. Therefore, the predictions of SDCPs are extremely important. Predictions within a data collection period are most commonly generated using fixed information about sample cases, and accumulating paradata and survey response data. Interventions occur during the data collection period, however, meaning they are applied based on predictions from incomplete accumulating data. There is evidence that the incomplete accumulating data can lead to biased and unstable predictions, particularly early in data collection. This dissertation explores the use of Bayesian methods to improve predictions of SDCPs during data collection, by providing a mathematical framework for combining priors, based on external data about covariates in the prediction models, with the current accumulating data to generate posterior predictions of SDCPs for use in intervention decisions.This dissertation includes three self-contained papers, each focused on the use of Bayesian methods to improve predictions of SDCPs for use in adaptive and responsive survey designs. The first paper predicts time to first contact, where priors are generated from historical survey data. The second paper implements expert elicitation, a method for prior construction when historical data is not available. The last paper describes a data collection experiment conducted using a Bayesian framework, which attempts to minimize data collection costs without reducing the quality of a key survey estimate. In all three papers, the use of Bayesian methods introduces modest improvements in the predictions of SDCPs, especially early in data collection, when interventions would have the largest effect on survey outcomes. Additionally, the experiment in the last paper resulted in significant data collection cost savings without having a significant effect on a key survey estimate. This work suggests that Bayesian methods can improve predictions of SDCPs that are critical for adaptive and responsive data collection interventions.
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    Design and Effectiveness of Multimodal Definitions in Online Surveys
    (2020) Spiegelman, Maura; Conrad, Frederick G; Survey Methodology; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    If survey respondents do not interpret a question as it was intended, they may, in effect, answer the wrong question, increasing the chances of inaccurate data. Researchers can bring respondents’ interpretations into alignment with what is intended by defining the terms that respondents might misunderstand. This dissertation explores strategies to increase response alignment with definitions in online surveys. In particular, I compare the impact of unimodal (either spoken or textual) to multimodal (both spoken and textual) definitions on question interpretation and, indirectly, response quality. These definitions can be further categorized as conventional or optimized for the mode in which they are presented (for textual definitions, fewer words than in conventional definitions with key information made visually salient and easier for respondents to grasp; for spoken definitions, a shorter, more colloquial style of speaking). The effectiveness of conventional and optimized definitions are compared, as well as the effectiveness of unimodal and multimodal definitions. Amazon MTurk workers were randomly assigned to one of six definition conditions in a 2x3 design: conventional or optimized definitions, presented in a spoken, textual, or multimodal (both spoken and textual) format. While responses for unimodal optimized and conventional definitions were similar, multimodal definitions, and particularly multimodal optimized definitions, resulted in responses with greater alignment with definitions. Although complementary information presented in different modes can increase comprehension and lead to increased data quality, redundant or otherwise untailored multimodal information may not have the same positive effects. Even as not all respondents complied with instructions to read and/or listen to definitions, the compliance rates and effectiveness of multimodal presentation were sufficiently high to show improvements in data quality, and the effectiveness of multimodal definitions increased when only compliant observations were considered. Multimodal communication in a typically visual medium (such as web surveys) may increase the amount of time needed to complete a questionnaire, but respondents did not consider their use to be burdensome or otherwise unsatisfactory. While further techniques could be used to help increase respondent compliance with instructions, this study suggests that multimodal definitions, when thoughtfully designed, can improve data quality without negatively impacting respondents.
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    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.
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    The Use of Email in Establishment Surveys
    (2019) Langeland, Joshua Lee; Abraham, Katharine; Wagner, James; Survey Methodology; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    This dissertation evaluates the effectiveness of using Email for survey solicitation, nonresponse follow-up, and notifications for upcoming scheduled interviews in an establishment survey setting. Reasons for interest in the use of Email include the possibility that it could reduce printing and postage expenses, speed responses and encourage online reporting. To date, however, there has been limited research on the extent to which these benefits can in fact be realized in an establishment survey context. In order to send an Email for survey purposes, those administering a survey must have Email addresses for the units in the sample. One method for collecting Email addresses is to send a prenotification letter to sampled businesses prior to the initial survey invitation, informing respondents about the upcoming survey and requesting they provide contact information for someone within the organization who will have knowledge of the survey topic. Relatively little is known, however, about what makes a prenotification letter more or less effective. The first experiment on which this dissertation reports varies the content of prenotification letters sent to establishments selected for participation in a business survey in order to identify how different features affect the probability of obtaining a respondent's Email address. In this experiment, neither survey sponsorship, appeal type, nor a message about saving taxpayer dollars had a significant impact on response. The second experiment is a pilot study designed to compare the results of sending an initial Email invitation to participate in an establishment survey to the results of sending a standard postal invitation. Sampled businesses that provided an Email address were randomized into two groups. Half of the units in the experiment received the initial survey invitation by Email and the other half received the standard survey materials through postal mail; all units received the same nonresponse follow-up treatments. The analysis of this experiment focuses on response rates, timeliness of response, mode of response and cost per response. In this production environment, Email invitations achieved an equivalent response rate at reduced cost per response. Units receiving the Email invitation were more likely to report online, but it took them longer on average to respond. The third experiment built on the second and was an investigation into nonresponse follow-up procedures. In the second experiment, at the point when the cohort that received the initial survey invitation by Email received their first nonresponse follow-up, there was a large increase in response. The third experiment tests whether this large increase in response can be achieved by sending a follow-up Email instead of a postal reminder. Sampled units that provided an Email address were randomized into three groups. All units received the initial survey invitation by Email and all units also received nonresponse follow-ups by Email. The treatments varied in the point in the nonresponse follow-up period at which the Emails were augmented with a postal mailing. The analysis focuses on how this timing affects response rates and mode of response. The sequence that introduced postal mail early in nonresponse follow-up achieved the highest final response rate. All mode sequences were successful in encouraging online data reporting. The fourth and final experiment studies the use of Email in a monthly business panel survey conducted through Computer Assisted Telephone Interviewing (CATI). After the first month in which an interviewer in this survey collects data from a business, she schedules a date to call and collect data the following month. The current procedure is to send a postcard to the business a few days prior to the scheduled appointment to serve as a reminder of the upcoming interview. The fourth experiment investigates the effects of replacing this reminder postcard with an Email. Businesses in a sample that included both businesses for which the survey organization had an Email address and businesses for which no Email address was available were randomized into three groups. The first group acts as the control and received the standard postcard; the second group was designated to receive an Email reminder, provided an Email address was available, instead of the postcard; and the third group received an Email reminder with an iCalendar attachment instead of the postcard, again provided an Email address was available. Results focus on response rates, call length, percent of units reporting on time, and number of calls to respondents. The experiment found that the use of Email as a reminder for a scheduled interview significantly increased response rates and decreased the effort required to collect data.