Research
My research examines social stratification in the United States including economic, health, and educational stratification. My current projects are outlined below.
Dissertation
My dissertation research focuses on the inequality faced by the Latinx population by examining an axis of difference within it: race. I examine racial diversity within the panethnic Latinx population to understand if Latinx individuals are a collective minority, assimilated to whiteness, occupying a distinctive position between Black and White people, or occupying all three roles simultaneously. I focus on economic and health outcomes and examine whether reception context, national climate, and neighborhood characteristics differentially relate to these disparities by racialized groups using social media data and geospatial methods.
To answer this overarching question, my doctoral dissertation includes three empirical chapters asking the following questions:
(1) How does the relation between anti-Latinx prejudice and income vary by race within the Latinx population?
(2) How do changes in national attitudes toward Latinx people differentially impact the mental health of Latinx racialized groups?
(3) How is racial and ethnic residential segregation differentially associated with income for Latinx people from different races?
I use American Community Survey (ACS) data, Behavioral Risk Factor Surveillance System (BRFSS) data, and Twitter Data to answer these questions utilizing natural language processing, spatial methods, and hierarchical linear modeling. I measure local and national attitudes towards racial/ethnic minority groups using a dataset of approximately 15 million Tweets that I collected using the Academic Twitter API prior to its depreciation. I also measure segregation using ACS data to calculate dissimilarity, isolation, and entropy indices.
Dissertation Committee: Dr. Erin Hamilton (Chair), Dr. Jacob Hibel, Dr. Noli Brazil, and Dr. Caitlin Patler (Outside Committee Member)
De Facto Deporation
In this project, with Drs.Erin Hamilton,Claudia Masferrer, andNicole Denier, we estimated binational immigrant populations (US-born children in Mexico, Mexican-born children in Mexico, US-born children of Mexican descent in the U.S., and Mexican-born children in the U.S.) to assess de facto deported populations and compare health, economic, and educational outcomes of these four groups. De facto deportation refers to US born children who return to Mexico because their parents are deported. I translated and re-coded the Mexican Census data for comparison with the American Community Survey to estimate the sizes and characteristics of these four populations. This paper has been invited for submission to a Russell Sage Foundation special issue titled Deportation and its Aftermath.
California Education
I work with Dr. Jacob Hibel in conjunction withCalPEPAL to examine educational equity in California using Local Control Funding Formula (LCFF) documents. In this project, we use topic modeling and document clustering to analyze trends in how districts discuss higher-needs students. In furtherance of this, I facilitated the creation of a database of 8 years of LCFF documents for ~850 school districts in California using R and utilized natural language processing techniques for data analysis.
Racial Health Disparities on Twitter
I work with Eugene Jang, Hannah Kinzer, and Minghui Wang to analyze (1) if/how racial health disparities are discussed on Twitter and (2) if these discussions are consistent with racial health disparities discussed in academic literature. We conduct topic modeling using LDA and BERT on Twitter Data and all peer-reviewed academic articles discussing racial health disparities from 2015 to 2019 to understand how the public and academics are discussing racial health disparities and if academic literature is informing public understanding of racial health disparities.
#SayHerName Discourse
I work with Dr. Laura Grindstaff, Colleen Sargent, Larissa Saco, and Elizabeth Witcher on a mixed-methods project using content analysis and computational social science methods to examine #SayHerName on Twitter from 2015 through 2020. This work examined the intent and drift of the hashtag over time, focusing on how people utilized it to express emotion and discuss racial inequalities.