Qualitative Methods

The Case for Including Disability in Social Science Demographics

October 15, 2024
by Mango Jane Angar. As we celebrate Disability Awareness Month at the D-Lab alongside the UC Berkeley scholarly community, how can we, as social scientists, individually promote accessibility and inclusion? To advance accessibility, we should focus on addressing the barriers faced by individuals with disabilities, using our research to provide insights for effective policy recommendations. Although most of us do not focus on disability-related issues, including disability as a demographic characteristic in our data collection can greatly enhance our understanding of diverse populations and improve the comprehensiveness of our analyses. This small step can contribute to broader efforts toward inclusion and social equity.

Institutional Review Board (IRB) Fundamentals

October 17, 2024, 3:00pm
Are you starting a research project at UC Berkeley that involves human subjects? If so, one of the first steps you will need to take is getting IRB approval.

Leveraging Large Language Models for Analyzing Judicial Disparities in China

October 8, 2024
by Nanqin Ying. This study analyzes over 50 million judicial decisions from China’s Supreme People’s Court to examine disparities in legal representation and their impact on sentencing across provinces. Focusing on 290 000 drug-related cases, it employs large language models to differentiate between private attorneys and public defenders and assess their sentencing outcomes. The methodology combines advanced text processing with statistical analysis, using clustering to categorize cases by province and representation, and regression models to isolate the effect of legal representation from factors like drug quantity and regional policies. Findings reveal significant regional disparities in legal access driven by economic conditions, highlighting the need for reforms in China’s legal aid system to ensure equitable representation for marginalized groups and promote transparent judicial data for systemic improvements.

Causal Inference in International Political Economy: Hurdles and Advancements

September 9, 2024
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
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.