Qualitative Methods

Berkeley FSRDC Fundamentals

January 31, 2024, 11:00am
Interested in restricted Census or partnering RDC agency (AHRQ, BLS, BEA, NCHS) data use? This one-hour introductory workshop will provide an overview of the Berkeley Federal Statistical Research Data Center, with no prior experience assumed. Attendees will learn about the national RDC network, how to access information online about restricted Census data, and how to navigate proposal development.

GPT Fundamentals

April 17, 2024, 3:00pm
This workshop offers a general introduction to the GPT (Generative Pretrained Transformers) model. We will explore how they reflect and shape our cultural narratives and social interactions, and which drawbacks and constraints they have.

Institutional Review Board (IRB) Fundamentals

February 7, 2023, 10:00am
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.

Survey Fundamentals

April 11, 2024, 3:00pm
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.

QDA Campus License Focus Group

October 12, 2023, 12:00pm
Calling All Qualitative & Mixed-Methods Researchers at UC Berkeley! Join the conversation on Qualitative Data Analysis (QDA) Campus Software License Options! Are you a researcher (undergraduate, graduate, or faculty/staff) at UC Berkeley who employs qualitative data, text analysis, or mixed-methods research approaches? If you rely on specialized software like Atlas.ti, NVivo, MaxQDA, Dedoose, or Otter.ai in your work, Research IT & D-Lab want your input to inform the future of qualitative research supports at UC Berkeley.

Jonathan Pérez

Data Science for Social Justice Fellow 2024
Education

Jonathan Pérez is a 4th year PhD student in education with a designated emphasis critical theory. His research focuses on how students understand their radicalization with a focus particularly on how California's Ethnic Studies Curriculum can equip students to better make sense of how schools and society racialize them.

Outside of of UC Berkeley, Jonathan is an adjunct at San Francisco State University and works in curriculum design for The School of The New York Times.

Conceptual Mirrors: Reflecting on LLMs' Interpretations of Ideas

April 23, 2024
María Martín López
by María Martín López. As large language models begin to engrain themselves in our daily lives we must leverage cognitive psychology to explore the understanding that these algorithms have of our world and the people they interact with. LLMs give us new insights into how conceptual representations are formed given the limitations of data modalities they have access to. Is language enough for these models to conceptualize the world? If so, what conceptualizations do they have of us?

Transparency in Experimental Political Science Research

April 9, 2024
Kamya Yadav
by Kamya Yadav. With the increase in studies with experiments in political science research, there are concerns about research transparency, particularly around reporting results from studies that contradict or do not find evidence for proposed theories (commonly called “null results”). To encourage publication of results with null results, political scientists have turned to pre-registering their experiments, be it online survey experiments or large-scale experiments conducted in the field. What does pre-registration look like and how can it help during data analysis and publication?

Hilary Faxon, Ph.D.

Data Science Fellow
Environmental Science, Policy, and Management
Dr. Faxon is an ethnographer who uses social media and critical remote sensing to understand and reimagine social justice in technology, environment, and development in the Global South. She is an Assistant Professor of Environmental Social Science at the University of Montana.

Introduction to Propensity Score Matching with MatchIt

April 1, 2024
Alex Ramiller
by Alex Ramiller. When working with observational (i.e. non-experimental) data, it is often challenging to establish the existence of causal relationships between interventions and outcomes. Propensity Score Matching (PSM) provides a powerful tool for causal inference with observational data, enabling the creation of comparable groups that allow us to directly measure the impact of an intervention. This blog post introduces MatchIt – a software package that provides all of the necessary tools for conducting Propensity Score Matching in R – and provides step-by-step instructions on how to conduct and evaluate matches.