Qualitative Methods

Causal Inference in International Political Economy: Hurdles and Advancements

September 9, 2024
Yue Lin
by Yue Lin. What are the key challenges and opportunities of applying experiments in the International Political Economy (IPE) research? In this blog, I reviewed an enduring methodological battle between statistics and experiments, and pointed out that the difficulties of randomization and locating credible counterfactuals have served as main hurdles for IPE scholars to widely adopt experimental tools. However, I further demonstrated some new progress in applying survey, field, and lab experiments in the recent IPE scholarship. I concluded that it is crucial for future researchers to think innovatively about how to combine different research methods to make causal claims in IPE studies.

Sakina Dhorajiwala

Availability: By appointment only

Consulting Areas: Python, R, Stata, LaTeX, Data Manipulation and Cleaning, Data Visualization, Mixed Methods, Qualitative Methods, Surveys, Sampling & Interviews, Regression Analysis, Excel, Git or Github, RStudio

Claudia von Vacano, Ph.D.

Availability: By appointment only

Consulting Areas: Digital Humanities, Mixed Methods, Qualitative methods, Surveys, Sampling & Interviews, MaxQDA, Career Development

Yue Lin

Data Science Fellow 2024-2025
Political Science

Yue is a Ph.D. student in Political Science at the University of California, Berkeley, with a Designated Emphasis on Political Economy. Using mixed methods, she studies foreign lobbying, geopolitical risk, and economic security to understand when, how, and why multinational corporations become the targets and weapons of state power rivalry.

Data for a Just U.S. - Using Data Science to Empower Marginalized Communities

September 3, 2024
Elijah Mercer
by Elijah Mercer. In this blog post, I share how working with marginalized communities through data science has transformed my understanding of the field. My journey from crime analysis to founding Data for Just US reveals the profound impact data can have when used to empower and uplift underserved populations. I explore the challenges and rewards of this work, illustrating how data science can drive social change and foster a more equitable future.

Deya Chic

Data Science for Social Justice Fellow 2024
Graduate School of Education

Deya is deeply committed to supporting underrepresented students and contributing to policies that address oppression in higher education. She aims to influence fellow professionals and researchers to adopt a comprehensive approach to addressing systemic issues in the higher education system.

Gesean Lewis

Data Science for Social Justice Fellow 2024
Education

Fifth year doctoral candidate in the Joint Special Education Program. Decade long resume in autism research and socialization. Currently working on my dissertation following the educational journeys of former foster youth on the autism spectrum.

Hellina Hailu Nigatu

Data Science for Social Justice Senior Fellow 2024
Electrical Engineering and Computer Science (EECS)

I am a PhD student at UC Berkeley in the EECS department co-advised by Prof. Sarah Chasins and Prof. John Canny. My research interest broadly lies in the intersection of AI and HCI, with a focus on making usable AI tools accessible to end users.

I am currently looking into making NLP tools usable and accessible for low-resourced languages. I am also interested in the impact of AI on society, specifically in how it affects Global Majority countries and communities. Outside of research, I like to read books, make and drink traditional Ethiopian coffee, knit,...

Minding the Gaps: Pay Equity in California

July 9, 2024
Tonya D. Lindsey, Ph.D.
by Tonya D. Lindsey, Ph.D. The gender pay gap continues to reflect that, on average, men outearn women. California is among the states with the smallest pay gaps (outpacing the national number at 13%) and is unique in that it enacted legislation aimed at eliminating pay gaps by sex and race categories. This blog post reflects on California’s pay gap as students study it in an undergraduate social statistics course. Independent variables indicate three theoretical frameworks: 1) human capital, 2) occupational segregation, and 3) discrimination. While the work students do is rigorous using a representative sample of full-time year-round California workers, there remains work to be done and caveats to the data and analyses.

Survey Fundamentals

February 21, 2024, 1:00pm
This two-hour workshop offers a comprehensive introduction to designing and conducting survey studies. Tailored for beginners, it provides clear, step-by-step guidance complemented by concise examples, practical considerations, and useful support materials. Participants will learn the entire process, from formulating a research question to creating, administering, and analyzing surveys, as well as interpreting results and communicating their findings.