Research Planning

Elaine Luo

Data Science Fellow 2024-2025
Graduate School of Education

Elaine (Hua) Luo is a PhD candidate in the Graduate School of Education, School Psychology PhD program. Her research interests focus on adolescents’ identity development and well-being under the transactional influence of entities in their socio-ecological systems. In her research, Elaine has utilized not only quantitative but also qualitative and mixed methods to study her research topics of interest. Before coming to Berkeley, Elaine earned her Master’s in Human Development and Psychology from Harvard Graduate School of Education and her Bachelor of Art in Education Sciences from...

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.

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 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 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

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.

Enhancing Research Transparency Inspired by Grounded Theory

April 30, 2024
Farnam Mohebi
by Farnam Mohebi. Grounded theory, a powerful tool for qualitative analysis, can enhance data science research by improving transparency and impact. Researchers can create a vivid record of their process by meticulously documenting the entire research journey, including the decisions they make and the corresponding rationale behind them, from initial data exploration to developing and refining theories. Embracing grounded theory principles, such as iterative coding and constant comparison, can help data scientists build robust, data-driven theories while ensuring transparency throughout the research process. This approach makes research more replicable and understandable and invites others to engage with the work, fostering collaboration and constructive critique, ultimately elevating the value and reach of their findings.

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?

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.

Computational Social Science in a Social World: Challenges and Opportunities

March 26, 2024
José Aveldanes
by José Aveldanes. The rise of AI, Machine Learning, and Data Science are harbingers of the need for a significant shift in social science research. Computational Social Science enables us to go beyond traditional methods such as Ordinary Least Squares, which face challenges in addressing complexities of social phenomena, particularly in modeling nonlinear relationships and managing high-dimensionality data. This paradigmatic shift requires that we embrace these new tools to understand social life and necessitates understanding methodological and ethical challenges, including bias and representation. The integration of these technologies into social science research calls for a collaborative approach among social scientists, technologists, and policymakers to navigate the associated risk and possibilities of these new tools.