Research Design

From Asking Causal Questions to Making Causal Inference

December 5, 2023
Lauren Liao
by Lauren Liao. What is causality and how do we ask causal questions? It may seem like a difficult and foreign concept, but fear not, I will guide you through the basic concepts in this blog post. We will start from how to ask causal questions then more formally address how to answer these questions. You may find causality more approachable than you think. It follows the same ideas as presented by the scientific method of rigorously testing how interventions produce different outcomes in a controlled environment.

Introduction to Item Response Theory

October 24, 2023
Mingfeng Xue
by Mingfeng Xue. Measurements (e.g., tests, surveys, questionnaires) are inevitably involved with various sources of errors. Among many psychometric theories, item response theory stands out for its capability of detailed analyses at the item level and its potential to reduce some of the measurement errors. This post first discussed the limitations of conventional summation and average, which give rise to the IRT models, and then introduced a basic form of the Rasch model, including expressions of the model, the assumptions underlying it, some of its advantages, and software packages. Some codes are also provided.

Americanist Linguistics: on Ethics and Intent

October 17, 2023
Anna Bjorklund
by Anna Björklund. In this post, Anna Björklund investigates the origin of the linguistic study of indigenous American languages, its inextricable ties to settler-colonialism, and how linguistics can move forward as a field.

FSRDC 2023 Annual Meeting and Research Conference

October 2, 2023
Renee Starowicz, Ph.D.
by Renee Starowicz. Renee Starowicz, Co-Executive Director of the Berkeley Federal Statistical Research Data Center, provides an overview of the takeaways from the 2023 Annual Federal Statistical Research Data Center Business Meeting and Annual Conference. She provides a brief overview of the Berkeley FSRDC. Then, she describes the priorities for collaboration across national directors to improve outreach to diverse researchers and transparency. Additionally, she points out the other key topics of conversation at this year’s meeting.

Introduction to Field Experiments and Randomized Controlled Trials

July 24, 2023
Leena Bhai
by Leena Bhai. This blog post provides an introduction to field experimentation and its significance in understanding cause and effect. It explains how randomized experiments represent an unbiased method for determining what works. It delves into essential features of experiments such as intervention, excludability, and non-interference. It then works through a fictional example of a randomized controlled trial of the efficacy of an experimental drug Covi-Mapp.

Understanding how organizational structures interact with psychology to influence academic-related behavior

September 8, 2021
Michael Ruiz
The ways in which educational organizations develop programs, approach pedagogy, and emphasize community building result in similarities and differences across different organizations’ structures. However, past research hasn’t developed a conceptual framework for understanding how differences in organizational structures might influence the educational outcomes of students from different backgrounds. The D-Lab NSF IUSE (Improving Undergraduate STEM Education) team sought to develop such a framework by leveraging past research from the fields of education, history, psychology, and sociology.

Assessing the Effectiveness of a Social Norms-Based Sexual Violence Prevention Digital Campaign on the UC Berkeley Campus

August 31, 2021
Swetha Pola
In collaboration with the prevention team at the PATH To Care Center (PTC) at the University of California, Berkeley, we experimentally assess the effectiveness of a sexual violence & sexual harassment (SVSH) prevention social media campaign on perceived social norms. Content Warning: This blog post mentions sexual violence & sexual harassment (SVSH)

Julia Lane, Ph.D.

Guest Speaker
Professor at the NYU Wagner Graduate School of Public Service
Professor at the NYU Center for Urban Science and Progress
NYU Provostial Fellow for Innovation Analytics

Julia Lane is a Professor at the NYU Wagner Graduate School of Public Service, at the NYU Center for Urban Science and Progress, and a NYU Provostial Fellow for Innovation Analytics. She cofounded the Coleridge Initiative, whose goal is to use data to transform the way governments access and use data for the social good through training programs, research projects and a secure data facility. The approach is attracting national attention, including the ...

Organized Code Repositories Accelerate Science and Facilitate Reproducubility

March 2, 2021
Pratik Sachdeva, Ph.D.

Computational and data-driven research increasingly requires developing complex codebases. At the same time, many scientists don’t receive training in software engineering practices, resulting in, for some, the perception that scientists write terrible software. As scientists, good software should accelerate our work and facilitate its reproducibility. While building good coding practices takes some time and experience, it doesn’t require a...

The Importance of Design Plans for Data Science

April 20, 2021
Alex Stephenson

Since becoming a Data Fellow at the D-Lab, I have had the opportunity to assist many talented social scientists through the D-Lab’s Consulting service. A regular consulting request is to help with the research design for a new project. These requests are understandable. For empirical researchers, a high-quality research design makes or breaks a research project. In this post, I suggest a few benefits of writing a skeleton design plan before writing any code whatsoever.

One of the exciting aspects...