Research Project

Minding the Gaps: Pay Equity in California

July 9, 2024
by Tonya D. Lindsey, Ph.D. The gender pay gap continues to reflect that, on average, men outearn women. California is among the states with the smallest pay gaps (outpacing the national number at 13%) and is unique in that it enacted legislation aimed at eliminating pay gaps by sex and race categories. This blog post reflects on California’s pay gap as students study it in an undergraduate social statistics course. Independent variables indicate three theoretical frameworks: 1) human capital, 2) occupational segregation, and 3) discrimination. While the work students do is rigorous using a representative sample of full-time year-round California workers, there remains work to be done and caveats to the data and analyses.

Institutional Review Board (IRB) Fundamentals

November 7, 2022, 12:00pm
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.

Institutional Review Board (IRB) Fundamentals

October 9, 2023, 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.

Institutional Review Board (IRB) Fundamentals

February 16, 2024, 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.

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.

Institutional Review Boards (IRB) Fundamentals

March 17, 2022, 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.

Sand Mining - Plugging a Critical Data Gap

May 14, 2024
by Suraj Nair. Excessive sand mining is causing a global ecological crisis. In this blog post, I present why sand mining is one of the most pressing challenges facing the planet, and why persistent data gaps hinder accountability and monitoring. I also discuss an ongoing research project of mine where we combine freely available satellite imagery and machine learning models to build open-source sand mine detection tools that can plug some of these data gaps.

On the Transformative Power of Seeing Others

May 7, 2024
by Daniel Lobo. Daniel Lobo, a PhD Student in Sociology at UC Berkeley, discusses his journey from growing up in the urban working class to making it to Harvard College and UC Berkeley. He credits his mentors who were able to see him in a way that he could not see himself as the keys to his success. This gift, the power to see others for who they are and who they could be, animates his research and teaching, including on the NSF-IUSE project.

Transparency in Experimental Political Science Research

April 9, 2024
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?

A Basic Introduction to Hierarchical Linear Modeling

March 4, 2024
by Mingfeng Xue. Hierarchical Linear Modeling (HLM) is an extension of linear models, which offers an approach to analyzing data structures with nested levels. This blog elucidates HLM's significance over traditional linear regression models, particularly in handling clustered data and multilevel predictors. Illustrated with an example from educational research, the blog demonstrates model implementation and interpretation steps. It showcases how HLM accommodates both independent variables from different levels and hierarchical structure data, providing insights into their impacts on the outcome variable. Recommended resources further aid readers in mastering HLM techniques.