Qualitative Analysis

Why Data Disaggregation Matters: Exploring the Diversity of Asian American Economic Outcomes Using Public Use Microdata Sample (PUMS) Data

February 11, 2025
by Taesoo Song. Asian Americans are often overlooked in discussions of racial inequality due to their high average socioeconomic attainment. Many academic and policy researchers treat Asians as a single racial category in their analysis. However, this broad categorization can mask significant within-group disparities, leaving many disadvantaged individuals without access to vital resources and policy support. Song emphasizes the importance of data disaggregation in revealing Asian American inequalities, particularly in areas like income and homeownership, and demonstrates how breaking down these categories can lead to more targeted and effective policy solutions.

Claudia von Vacano, Ph.D.

Founding Executive Director, P.I., Research Director, FSRDC

Dr. Claudia von Vacano is the Founding Executive Director and Senior Research Associate of D-Lab and Digital Humanities at Berkeley and is on the boards of the Social Science Matrix and Berkeley Center for New Media. She has worked in policy and educational administration since 2000, and at the UC Office of the President and UC Berkeley since 2008. She received a Master’s degree from Stanford University in Learning, Design, and Technology. Her doctorate is in Policy, Organizations, Measurement, and Evaluation from UC Berkeley. Her expertise is in organizational theory and...

Field Experiments in Corporations

January 28, 2025
by Yue Lin. How do social science researchers conduct field experiments with private actors? Yue Lin provides a brief overview of the recent developments in political economy and management strategy, with a focus on filing field experiments within private corporations. Unlike conventional targets like individuals and government agencies, private companies are an emergent sweet spot for scholars to test for important theories, such as sustainability, censorship, and market behavior. After comparing the strengths and weaknesses of this powerful yet nascent method, Lin brainstorms some practical solutions to improve the success rate of field experimental studies. She aims to introduce a new methodological tool in a nascent research field and shed some light on improving experimental quality while adhering to ethical standards.

Fritz_X_DargesBlue42… Who Are You?

January 14, 2025
by Jonathan Pérez. Reflecting on the complexities of the human experience is paramount to conducting research. Jonathan Pérez, through his exploration of a conspiracy subreddit, reflects on his experience trying to find the human behind the datum. Jonathan critiques the harmful effects of dehumanizing rhetoric and the researcher’s responsibility to navigate ethical implications. In doing so, he establishes three guiding rules to support researchers seeking to humanize their analysis: 1) a researcher must always find the story behind the data; 2) a researcher must protect themselves; 3) a researcher must still humanize participants (even those who perpetuate harmful narratives).

What are Time Series Made of?

December 10, 2024
by Bruno Smaniotto. Trend-cycle decompositions are statistical tools that help us understand the different components of Time Series – Trend, Cycle, Seasonal, and Error. In this blog post, we will provide an introduction to these methods, focusing on the intuition behind the definition of the different components, providing real-life examples and discussing applications.

Language Models in Mental Health Conversations – How Empathetic Are They Really?

December 3, 2024
by Sohail Khan. Language models are becoming integral to daily life as trusted sources of advice. While their utility has expanded from simple tasks like text summarization to more complex interactions, the empathetic quality of their responses is crucial. This article explores methods to assess the emotional appropriateness of these models, using metrics such as BLEU, ROUGE, and Sentence Transformers. By analyzing models like LLaMA in mental health dialogues, we learn that while they suffer through traditional word-based metrics, LLaMA's performance in capturing empathy through semantic similarity is promising. In addition, we must advocate for continuous monitoring to ensure these models support their users' mental well-being effectively.

A Recipe for Reliable Discoveries: Ensuring Stability Throughout Your Data Work

November 19, 2024
by Jaewon Saw. Imagine perfecting a favorite recipe, then sharing it with others, only to find their results differ because of small changes in tools or ingredients. How do you ensure the dish still reflects your original vision? This challenge captures the principle of stability in data science: achieving acceptable consistency in outcomes relative to reasonable perturbations of conditions and methods. In this blog post, I reflect on my research journey and share why grounding data work in stability is essential for reproducibility, adaptability, and trust in the final results.

Python Data Processing Basics for Acoustic Analysis

November 12, 2024
by Amber Galvano. Interested in learning how to merge data and metadata from multiple sources into a consolidated dataset? Dealing with annotated audio and want to automate your workflow? Tried Praat scripting but want something more streamlined? This blog post will walk through some key domain-specific Python-based tools you will need in order to take your audio data, annotations, and speaker metadata and come away with a tabular dataset containing acoustic measures, ready to visualize and submit to statistical analysis. This tutorial uses acoustic phonetics data, but can be adapted to a range of projects involving repeated measures data and/or work with audio files.

Human-Centered Design for Migrant Rights

October 29, 2024
by Victoria Hollingshead. In honor of the 2024 International Day of Care and Support, Victoria Hollingshead shares her recent work with the Center for Migrant Advocacy’s Direct Assistance Program and their innovative approach to supporting Overseas Filipino Workers (OFWs) using generative AI. OFWs, especially female domestic workers in the Gulf Cooperation Council (GCC), are vulnerable to exploitation from foreign employers and recruitment agencies while having limited access to legal support. Using a design thinking framework, Victoria and CMA’s Direct Assistance team co-designed a proof of concept to enhance the legal and contract literacy among OFWs in the Kingdom of Saudi Arabia, a top destination country. This project shows promise in leveraging emerging technologies to empower OFWs, enhancing the Philippines' reputation as a migrant champion and supporting the nation's broader push for digital transformation.

Concepts and Measurements in Social Network Analysis

October 22, 2024
by Christian Caballero. We live in an interconnected world, more so now than ever. Social Network Analysis (SNA) provides a toolkit to study the influence of this interconnectivity. This blog post introduces some key theoretical concepts behind SNA, as well as a family of metrics for measuring influence in a network, known as centrality. These concepts and measurements help form the basis for a theoretically informed study of social relationships in an era where the availability of relational data has dramatically increased thanks to technological advances.