Means Tests

Design Your Observational Study with the Joint Variable Importance Plot

March 12, 2024
Lauren Liao
by Lauren Liao. When evaluating causal inference in observational studies, there often is a natural imbalance in the data. Luckily, variables are often measured alongside that can be helpful for adjustment. However, deciding which variables should be prioritized for adjustment is not trivial – since not all variables are equally important to the intervention or the outcome. I recommend using the joint variable importance plot during the observational study design phase to visualize which variables should be prioritized. This post provides a gentle guide on how to do so and why it is important.

Jane Angar

Consulting Drop-In Hours: Wed 9am-11am

Consulting Areas: R, Stata, LaTeX, Data Manipulation and Cleaning, Data Visualization, Qualitative methods, R Programming, Regression Analysis, Means Tests, Excel, Git or Github, Qualtrics, RStudio, Stata, Jupiter Notebook

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Creating the Ultimate Sweet

January 30, 2024
Emma Turtelboom
by Emma Turtelboom. What is the best Halloween candy? In this blog post, we will identify attributes of popular sweets and create a model to understand how these attributes influence the popularity of the sweet. We’ll discuss alternative model approaches and potential drawbacks, as well as caveats to interpreting the predictions of our model.

Reine Ngnonsse

IUSE Undergraduate Advisory Board
Genetics and Plant Biology

Reine Ngnonsse, an enthusiast for math and technology, delved into tutoring math at a community college through the EOPs program. At UC Berkeley, while pursuing Genetics and Plant Biology, She explored R programming in a CRISPR project. As an intern at Health Career Connection, Reine expanded coding skills in Python, R, and Tableau, igniting a passion for programming. With exposure to Python and Javascript, she can't wait to merge mathematical prowess with coding finesse for innovative solutions.

Exploratory Data Analysis in Social Science Research

November 14, 2023
Kamya Yadav
by Kamya Yadav. Causal inference has become the dominant endeavor for many political scientists, often at the expense of good research questions and theory building. Returning to descriptive inference – the process of describing the world as it exists – can help formulate research questions worth asking and theory that is grounded in reality. Exploratory data analysis is one method of conducting descriptive inference. It can help social science researchers find empirical patterns and puzzles that motivate their research questions, test correlations between variables, and engage with the existing literature on a topic. In this blog post, I walk through results from exploratory data analysis I conducted for my dissertation project on political ambition of women.

María Martín López

Data Science Fellow
Psychology

María Martín López is a PhD student in the Cognition area within the Department of Psychology. Her research relates to cognitive computational and quantitative models of individual differences in behaviors, thoughts, and emotions. She is particularly interested in how we can create and leverage novel algorithms to understand, measure, and predict processes relating to externalizing psychopathology (e.g. impulsivity, aggression, substance use). She answers these questions using a range of computational and quantitive models including AI, NLP, SEM, time series analysis, multi-level...

James Hall

Consultant
Department of Statistics

James Hall is a graduate student in the Statistics MA program at University of California, Berkeley. He is a husband and father to three awesome kids. Originally from Baltimore, MD, James earned his bachelors in Mathematics at the United States Military Academy at West Point, NY in 2011, and served as a U.S. Army officer. He’s served as a leader at multiple levels within large organizations with a professional focus on visualizing and communicating complex analysis to decision makers. James’ experience and coursework give him expertise in navigating different statistical methods,...

Michael Ruiz

IUSE Research Team
Psychology

Michael earned his B.A.in Psychology from UC Berkeley and currently works as the manager of Professor Okonofua's Equity, Diversity, and Empathy Navigation Sciences Lab in the UC Berkeley Psychology department.

Alex Ramiller

Data Science Fellow
City and Regional Planning

I am a PhD Candidate in City and Regional Planning. My research focuses on the use of large administrative datasets to study residential mobility, neighborhood change, and housing access. I received a Master in Geography from the University of Washington and a Bachelor's in Economics and Geography from Macalester College. I have also consulted on analytical projects for several organizations including the San Francisco Federal Reserve Bank, PolicyLink, and the City of Seattle.

Jane Angar

Consultant
Political Science

Mango Jane Angar is a Political Science Ph.D. student at the University of California, Berkeley. Her research focuses on Political Violence and Disability Politics. In particular, she examines how state institutions, society, and disabled persons organizations conceptualize and define disability.