Combining Information from Multiple Sources in Bayesian Modeling

Surveys can collect important data that inform policy decisions and drive social science research. Large government surveys collect information from the U.S. population on a wide range of topics, including demographics, education, employment, and lifestyle. Analysis of survey data presents unique challenges. In particular, one needs to account for missing data, for complex sampling designs, and for measurement error. Conceptually, a survey organization could spend lots of resources getting high-quality responses from a simple random sample, resulting in survey data that are easy to analyze. However, this scenario often is not realistic. To address these practical issues, survey organizations can leverage the information available from other sources of data. For example, in longitudinal studies that suffer from attrition, they can use the information from refreshment samples to correct for potential attrition bias. They can use information from known marginal distributions or survey design to improve inferences. They can use information from gold standard sources to correct for measurement error.

This thesis presents novel approaches to combining information from multiple sources that address the three problems described above.

The first method addresses nonignorable unit nonresponse and attrition in a panel survey with a refreshment sample. Panel surveys typically suffer from attrition, which can lead to biased inference when basing analysis only on cases that complete all waves of the panel. Unfortunately, the panel data alone cannot inform the extent of the bias due to attrition, so analysts must make strong and untestable assumptions about the missing data mechanism. Many panel studies also include refreshment samples, which are data collected from a random sample of new

individuals during some later wave of the panel. Refreshment samples offer information that can be utilized to correct for biases induced by nonignorable attrition while reducing reliance on strong assumptions about the attrition process. To date, these bias correction methods have not dealt with two key practical issues in panel studies: unit nonresponse in the initial wave of the panel and in the

refreshment sample itself. As we illustrate, nonignorable unit nonresponse

can significantly compromise the analyst's ability to use the refreshment samples for attrition bias correction. Thus, it is crucial for analysts to assess how sensitive their inferences---corrected for panel attrition---are to different assumptions about the nature of the unit nonresponse. We present an approach that facilitates such sensitivity analyses, both for suspected nonignorable unit nonresponse

in the initial wave and in the refreshment sample. We illustrate the approach using simulation studies and an analysis of data from the 2007-2008 Associated Press/Yahoo News election panel study.

The second method incorporates informative prior beliefs about

marginal probabilities into Bayesian latent class models for categorical data.

The basic idea is to append synthetic observations to the original data such that

(i) the empirical distributions of the desired margins match those of the prior beliefs, and (ii) the values of the remaining variables are left missing. The degree of prior uncertainty is controlled by the number of augmented records. Posterior inferences can be obtained via typical MCMC algorithms for latent class models, tailored to deal efficiently with the missing values in the concatenated data.

We illustrate the approach using a variety of simulations based on data from the American Community Survey, including an example of how augmented records can be used to fit latent class models to data from stratified samples.

The third method leverages the information from a gold standard survey to model reporting error. Survey data are subject to reporting error when respondents misunderstand the question or accidentally select the wrong response. Sometimes survey respondents knowingly select the wrong response, for example, by reporting a higher level of education than they actually have attained. We present an approach that allows an analyst to model reporting error by incorporating information from a gold standard survey. The analyst can specify various reporting error models and assess how sensitive their conclusions are to different assumptions about the reporting error process. We illustrate the approach using simulations based on data from the 1993 National Survey of College Graduates. We use the method to impute error-corrected educational attainments in the 2010 American Community Survey using the 2010 National Survey of College Graduates as the gold standard survey.

Revising Justice: Punitory Thought and Action in the Work of Atwood, Jordan, and Oates

*Designated as an exemplary master's project for 2015-16*

Human dimensions of bycatch reduction technology: Current assumptions and directions for future research

Bycatch reduction technology (BRT) modifies fishing gear to increase selectivity and avoid capture of non-target species, or to facilitate their non-lethal release. As a solution to fisheries-related mortality of non-target species, BRT is an attractive option; effectively implemented, BRT presents a technical 'fix' that can reduce pressure for politically contentious and economically detrimental interventions, such as fisheries closures. While a number of factors might contribute to effective implementation, our review of BRT literature finds that research has focused on technical design and experimental performance of individual technologies. In contrast, and with a few notable exceptions, research on the human and institutional context of BRT, and more specifically on how fishers respond to BRT, is limited. This is not to say that fisher attitudes are ignored or overlooked, but that incentives for fisher uptake of BRT are usually assumed rather than assessed or demonstrated. Three assumptions about fisher incentives dominate: (1) economic incentives will generate acceptance of BRT; (2) enforcement will generate compliance with BRT; and (3) 'participation' by fishers will increase acceptance and compliance, and overall support for BRT. In this paper, we explore evidence for and against these assumptions and situate our analysis in the wider social science literature on fisheries. Our goal is to highlight the need and suggest focal areas for further research. © Inter-Research 2008.

The Politics of Gender Socialization

This manuscript is comprised of three papers that examine the far-reaching and often invisible political outcomes of gender role socialization in the United States. These papers focus primarily on two areas: political confidence amongst girls and women, and the effects of gender on survey measurement and data quality.

Chapter one focuses on political confidence, and the likelihood that women will run for political office. Women continue to be underrepresented at all levels of political leadership, and their lack of political ambition, relative to men, has been identified as a primary cause. In this paper, I explore the relationship between an individual's masculinity and femininity and her development of political ambition. Using original survey data from the 2012 Cooperative Congressional Election Study (CCES), I first empirically demonstrate that gender (masculinity/femininity) and sex (male/female) are unique elements of identity and, moreover, are both independently related to political ambition. I then explore the relevance of gender for the study of candidate emergence, testing whether and how masculinity and femininity might be related to political ambition are supported empirically. While the results suggest that masculinity is positively associated with the development of political ambition, the relationship between femininity and candidate emergence seems to be more complicated and not what prevailing stereotypes might lead us to expect. Moreover, while the relationship between masculinity and political ambition is the same for men and women, the relationship between femininity and political ambition is very different for women than it is for men. This study suggests that gender role socialization is highly related with both men's and women's desire to seek positions of political leadership.

Chapter two continues this exploration of gendered differences in the development of political ambition, this time exploring how social attractiveness and gendered perceptions of political leadership impact the desire to hold political office.Women are persistently underrepresented as candidates for public office and remain underrepresented at all levels of government in the United States. Previous literature suggests that the gendered ambition gap, gender socialization, insufficient recruitment, media scrutiny, family responsibilities, modern campaign strategies, and political opportunity structures all contribute to the gender imbalance in pools of officeholders and candidates. To explain women's reticence to run, scholars have offered explanations addressing structural, institutional, and individual-level factors that deter women from becoming candidates, especially for high positions in the U.S. government. This paper examines a previously unexplored factor: how dating and socialized norms of sexual attraction affect political ambition. This study investigates whether young, single, and heterosexual women's desire for male attention and fear of being perceived as unattractive or "too ambitious" present obstacles to running for office. The results of these experiments suggest that social expectations about gender, attraction and sexuality, and political office-holding may contribute to women's reticence to pursue political leadership. Chapter two is a co-authored work and represents the joint efforts of Laura Lazarus Frankel, Shauna Shames, and Nadia Farjood.

Chapter 3 bridges survey methodology and gender socialization, focusing on how interviewer sex affects survey measurement and data quality. Specifically, this paper examines whether and how matching interviewer and respondent sex affects panel attrition--respondents dropping out of the study after participating in the first wave. While the majority of research on interviewer effects suggests that matching interviewer and respondent characteristics (homophily) yields higher quality data, little work has examined whether this pattern holds true in the area of panel attrition. Using paradata from the General Social Survey (GSS), I explore this question. My analysis reveals that, despite its broader positive effects on data quality, matching interviewer and respondent sex increases likelihood to attrit. Interestingly, this phenomenon only emerges amongst male respondents. However, while assigning female interviewers to male respondents decreases their propensity to attrit, it also increases the likelihood of biased responses on gender related items. These conflicting outcomes represent a tradeoff for scholars and survey researchers, requiring careful consideration of mode, content, and study goals when designing surveys and/or analyzing survey data. The implications of these patterns and areas for further research are discussed.

Together, these papers illustrate two ways that gender norms are related to political outcomes: they contribute to patterns of candidate emergence and affect the measurement of political attitudes and behaviors.