A Network Approach to Understanding Bias Victimization and Discrimination

• In Event: Innovative Applications of Network Analyses: People, Experiences, and Social Media

Abstract

Recently, network analytics have been exploding in popularity due to their ability to unpack nuanced relationships between like phenomena. Here, we attempt to better understand the role everyday discrimination plays in the broader landscape of bias-related violence, victimization, and harm. Through these network techniques, we are able to discern how each individual form of discrimination or other bias-related victimization affects the broader structure of bias-based victimization experiences. On a sample of 323 self-identifying Latino adults, we construct a network analysis on all bias-victimization and discrimination items. Centrality measures help to discern which specific forms of bias-victimization and discrimination most clearly define the broader structure of victimizing experiences. Additionally, community detection algorithms help to discern the clustering of these disparate victimization experiences. Implications for this study ultimately may affect future research on victimization, better uncovering a spectrum of victimizing experiences defined by different underlying mechanisms.

Authors

• Sarah Lockwood, University of South Florida

• Stephen Abeyta, New York University

• Carlos Cuevas, Northeastern University