Person:

Stewart, Brandon Michael

In examining the discussion of social and political phenomena like regime transition, conflict, and policy change, scholars routinely make choices about how proximity is defined and which neighbors should be considered more important than others. Since each specification offers an alternative view of the networks through which discussion can take place, one's decision can exert a significant influence on the magnitude and scope of estimated discussion effects. This problem is widely recognized, but is rarely the subject of direct analysis. In international relations research, connectivity choices are usually ad hoc, driven more by data availability than by theoretically informed decision criteria. We take a closer look at the assumptions behind these choices, and propose a more systematic method to asses the structural similarity of two or more alternative networks, and select one that most plausibly relates theory to empirics. We apply this method to the spread of democratic regime change, and offer an illustrative example of how neighbor choices might impact predictions and inferences in the case of the 2011 Arab Spring.

Objective: Undergraduates at a university in the United States were exposed – directly and indirectly – to 14 peer deaths during one academic year. We examined how individual and social factors were associated with psychological (e.g., anxiety, depression, somatization) and physiological (i.e., cortisol) distress responses following this unexpected and repeated experience with loss. Method Two to three months after the final peer death, respondents (N = 122, 61% female, 18–23 years, M = 20.13, SD = 1.14) reported prior adverse experiences, degree of closeness with the deceased, acute responses to the peer deaths, ongoing distress responses, social support, support seeking, and media viewing. A subset (n = 24) returned hair samples for evaluation of cortisol responses during the previous 3 months. Results: Ongoing psychological distress was associated with a) prior interpersonal trauma, b) fewer social supports, and c) media exposure to news of the deaths (p's<.05). Participants who had no prior bereavements showed, on average, high cortisol (>25 p/mg) compared to individuals with one or two prior bereavement experiences (who were, on average, within the normal range, 10 to 25 p/mg) (p<.05). Only 8% of the sample utilized available university psychological or physical health resources and support groups. Conclusions: Limited research has examined the psychological and physiological impact of exposure to chronic, repeated peer loss, despite the fact that there are groups of individuals (e.g., police, military soldiers) that routinely face such exposures. Prior adversity appears to play a role in shaping psychological and physiological responses to repeated loss. This topic warrants further research given the health implications of repeated loss for individuals in high-risk occupations and university settings.

This collection of three papers develops two statistical techniques for addressing canonical problems in applied computational social science: unsupervised text analysis and regression with dependent data. In both cases I provide a flexible framework that allows the analyst to leverage known structure within the data to improve inference. The first paper introduces the Structural Topic Model (STM) which generalizes and extends a broad class of probabilistic topic models developed in computer science. Crucially for applied social science, STM provides a framework for estimating the factors which drive topical frequency and content within documents. The second paper explores the challenge that non-convex likelihoods pose for applied research with topic models. The paper presents a series of diagnostics and discusses the under-appreciated role of initialization methods. The third paper introduces Latent Factor Regressions (LFR), a new set of tools for regression modeling in the presence of unobserved heterogeneity or dependence between observations. The approach uses interactive latent effects to provide a unified framework for modeling different data structures, including network, time-series cross-sectional and spatial data.

Each of these methods is designed with a focus on applied work. Estimation algorithms are presented which are fast enough for applied work and software is either currently available (STM) or in development (LFR). The use of these techniques is illustrated with a range of applications from across political science.