Objectives: We examine how news media portrays the causes of mass shootings for shooters of different races. Specifically, we explore whether White men are disproportionately framed as mentally ill, and what narratives media tend to invoke when covering mass shootings through the lens of mental illness as opposed to other explanatory frames. Methods: The study examines a unique data set of 433 news documents covering 219 mass shootings between January 1, 2013, and December 31, 2015. It analyzes the data using a mixed methods approach, combining logistic regression with content analysis. Results: Quantitative findings show that Whites and Latinos are more likely to have their crime attributed to mental illness than Blacks. Qualitative findings show that rhetoric within these discussions frame White men as sympathetic characters, while Black and Latino men
Threat theory argues that states toughen criminal laws to repress the competitive power of large minority groups. Yet, research on threat suffers from a poor understanding of why minority group size contributes to social control and a lack of evidence on whether criminal law is uniquely responsive to the political interests of majority racial groups at all. By compiling a unique state-level dataset on 230 sentencing policy changes during mass incarceration and using data from 257,362 responses to 79 national surveys to construct new state-level measures of racial differences in punitive policy support, I evaluate whether criminal sentencing law is uniquely responsive to white public policy interests. Pooled event history models and mediation analyses support three primary conclusions: (1) states adopted new sentencing policies as a nonlinear response to minority group size, (2) sentencing policies were adopted in response to white public, but not black public, support for punitive crime policy, and (3) minority group size and race-specific homicide victimization both indirectly affect sentencing policy by increasing white public punitive policy support. These findings support key theoretical propositions for the threat explanation of legal change and identify white public policy opinion as a mechanism linking minority group size to variation in criminal law.
Exponential random graph models (ERGM) have been widely applied in the social sciences in the past ten years. However, diagnostics for ERGM have lagged behind their use.Collinearity-type problems can emerge without detection when fitting ERGM, yielding inconsistent model estimates and problematizing inference from parameters. This article provides a method to detect multicollinearity in ERGM. It outlines the problem and provides a method to calculate the variance inflation factor from ERGM parameters. It then evaluates the method with a Monte Carlo simulation, fitting 216,000 ERGMs and calculating the variance inflation factors for each model. The distribution of variance inflation factors is analyzed using multilevel regression to determine what network characteristics lend themselves to collinearitytype problems. The relationship between variance inflation factors and unstable standard errors (a standard sign of collinearity) is also examined. The method is shown to effectively detect multicollinearity and guidelines for interpretation are discussed.
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