Qualitative Methods

Looking Ahead: How Adolescents’ Consideration of Future Consequences Shapes Their Developmental Outcomes

March 25, 2025
Elaine Luo
by Elaine Luo. Adolescents constantly balance immediate impulses with long-term goals. Our research explored how adolescents differ in their tendency to think about immediate versus future consequences, and how these differences relate to academic performance, stress, and perceived life chances. Using Latent Profile Analysis, we identified three distinct groups: Indifferent (low consideration overall), Future-Focused (prioritizing future outcomes), and Dual-Focused (high consideration of both immediate and future outcomes). Results indicated the Dual-Focused adolescents had higher academic achievement, whereas the Future-Focused group perceived the most positive life prospects. A discussion on practical implications and future research direction for supporting balanced decision-making among adolescents is also provided.

Nimita Gaggar

Consultant
Public Health

Passionate and driven Public Health graduate student at UC Berkeley with a strong background in program management and a relentless pursuit of excellence. I have 5+ years of experience in program management and operations in the healthcare industry. My academic journey at UC Berkeley has equipped me with a multifaceted skill set, blending strategic thinking, data-driven decision-making, and effective communication. I thrive in fast-paced, dynamic environments and have a proven ability to lead cross-functional teams toward project success.

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...

Fritz_X_DargesBlue42… Who Are You?

January 14, 2025
Jonathan Pérez
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).

Navigating AI Tools in Open Source Contributions: A Guide to Authentic Development

December 17, 2024
Sahiba Chopra
by Sahiba Chopra. The rise of ChatGPT has transformed how developers approach their work - but it might be hurting your reputation in the open-source community. While AI can supercharge your productivity, knowing when not to use it is just as crucial as knowing how to use it effectively. This guide reveals the unspoken rules of AI usage in open source, helping you navigate the fine line between leveraging AI and maintaining authenticity. Learn when to embrace AI tools and when to rely on your own expertise, plus get practical tips for building trust in the open-source community.

Institutional Review Board (IRB) Fundamentals

February 18, 2025, 9:00am
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.

What are Time Series Made of?

December 10, 2024
Bruno Smaniotto
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
Sohail Khan
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.

Python Data Processing Basics for Acoustic Analysis

November 12, 2024
Amber Galvano
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.

Concepts and Measurements in Social Network Analysis

October 22, 2024
Christian Caballero
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.