For a full list of all my publications and working papers, please see my CV.
Image analysis and computer vision: understanding political messages beyond words
Political communication is a central element of political dynamics. The visual component of it is crucial to understand the origin, characteristics and consequences of the messages sent between political organizations, media and citizens. However, visual features have been largely overlooked in political science despite their strong and frequent presence in political texts, media, campaigns and more. In this study I introduce to political science an approach to quantify and classify images, the Bag of Visual Words. Further, I illustrate the applicability of this method drawn from the computer vision literature by classifying images of protests of the Black Lives Matter movement according to the level of violence and conflict they depict. This accesible and intutive method that consists of building a ``visual vocabulary'' is one of the first attempts in political science to quantify and classify images. This process represents the first step to achieving a better understanding of the structure and impact of visual material on political attitudes and opinions. (Photo credit: Robert Cohen/St. Louis Post Dispatch)
How conditioning on post-treatment variables can ruin your experiment and what to do about it
with Jacob M. Montgomery and Brendan Nyhan
Accepted pending replication in the American Journal of Political Science
In principle, experiments offer a straightforward method for social scientists to accurately estimate causal effects. However, scholars often unwittingly distort treatment effect estimates by conditioning on variables that could be affected by their experimental manipulation. Typical examples include controlling for post-treatment variables in statistical models or eliminating observations based on post-treatment criteria. Though these modeling choices are intended to address common problems encountered when conducting experiments, they can create severe bias in estimates of causal effects. Moreover, problems associated with conditioning on post-treatment variables remain largely unrecognized in the field, which frequently publishes studies using these practices in our discipline's most prestigious journals. We demonstrate the magnitude of the potential distortions induced by post-treatment conditioning in experiments using Monte Carlo simulations and a reanalysis of real-world data before concluding with recommendations for best practice.
Estimating controlled direct effects through marginal structural models
The estimation of direct effects allows researchers to understand causal mechanisms. However, in political science research, the treatment often affects both the confounders of the mediator and the outcome. Under these conditions traditional regression methods typically lead to two types of biases: endogenous selection and post-treatment control. This article introduces marginal structural models (MSMs) which allows unbiased estimation of controlled direct effects. I compare the inferences made from MSMs versus a naïve model using simulations and two examples regarding the effects of conditions in early stages of life on political outcomes. The analyses show that MSMs improve our understanding of causal mechanisms especially when dealing with time-varying treatments and covariates in panel and longitudinal contexts.
Through the ideology of the beholder: partisan perceptions and polarization among the mass public
with Jonathan Homola, Jon C. Rogowski, Betsy Sinclair, and Patrick D. Tucker
Scholars have documented increased polarization among contemporary mass publics but its causes and consequences are understood less well. In this paper, we study how citizens develop and apply perceptions of partisan groups. Our theoretical perspective identifies attitudinal intensity rather than partisan identities as a key accelerant of affective and social polarization. Data from two waves of a nationally representative survey demonstrate that while individuals tend to hold inaccurate perceptions of partisan out-group members, these exaggerations are driven primarily by ideological extremity rather than group membership. In a second study, we use an original survey experiment to identify the consequences of exaggerated perceptions and find that realized perceptions---but not partisanship---significantly reduced the potential for interpersonal interaction across a range of settings. Our results suggest that how partisans are perceived contribute to and reinforce social polarization and have important implications for understanding how increased party polarization affects contemporary American politics.
Personality stability and politics: TIPI variability
with Joshua Boston, Jonathan Homola, Betsy Sinclair, and Patrick Tucker
Forthcoming in the Public Opinion Quarterly
Researchers frequently claim that personality traits, measured using the Ten Item Personality Inventory (TIPI) battery, affect Americans’ political attitudes and behaviors. Such studies often depend on two key assumptions: personality measurements display stability over time and predate political behaviors of interest. In this paper we employ new panel survey data to test these assumptions. First, we find modest levels of TIPI variability over time. Second, we associate an individual’s self-reported personality not only with socioeconomic and demographic characteristics, but also, and more concerning, variability in her political attitudes. While the stability of the TIPI instrument is encouraging, the association between politics and the TIPI instrument suggests that TIPI may vary in response to political events.