Statistics

Concepts and Measurements in Social Network Analysis

October 22, 2024
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.

Leveraging Large Language Models for Analyzing Judicial Disparities in China

October 8, 2024
by Nanqin Ying. This study analyzes over 50 million judicial decisions from China’s Supreme People’s Court to examine disparities in legal representation and their impact on sentencing across provinces. Focusing on 290 000 drug-related cases, it employs large language models to differentiate between private attorneys and public defenders and assess their sentencing outcomes. The methodology combines advanced text processing with statistical analysis, using clustering to categorize cases by province and representation, and regression models to isolate the effect of legal representation from factors like drug quantity and regional policies. Findings reveal significant regional disparities in legal access driven by economic conditions, highlighting the need for reforms in China’s legal aid system to ensure equitable representation for marginalized groups and promote transparent judicial data for systemic improvements.

Finley Golightly

IT Support & Helpdesk Supervisor
Applied Mathematics

Finley joined D-Lab as full-time staff launching their career in Data Science after graduating with a Bachelor's degree in Applied Math from UC Berkeley.

They have been with D-Lab since Fall 2020, formerly as part of the UTech Management team before joining as full-time staff in Fall 2023. They love the learning environment of D-Lab and their favorite part of the job is their co-workers! In their free time, they enjoy reading, boxing, listening to music, and playing Dungeons & Dragons. Feel free to stop by the front desk to ask them any questions or...

Causal Thinking in Thermal Comfort

September 17, 2024
by Ruiji Sun. We demonstrate the importance of causal thinking by comparing two linear regression approaches used in thermal comfort research: Approach (a), which regresses thermal sensation votes (y-axis) on indoor temperature (x-axis); Approach (b), which does the reverse, regressing indoor temperature (y-axis) on thermal sensation votes (x-axis). From a correlational perspective, they may appear interchangeable, but causal thinking reveals substantial and practical differences between them. Using the same data, we found Approach (b) leads to a 10 °C narrower than the conventionally derived comfort zone using Approach (a). This finding has important implications for occupant comfort and building energy efficiency. We highlight the importance of integrating causal thinking into correlation-based statistical methods, especially given the increasing volume of data in the built environment.

Causal Inference in International Political Economy: Hurdles and Advancements

September 9, 2024
by Yue Lin. What are the key challenges and opportunities of applying experiments in the International Political Economy (IPE) research? In this blog, I reviewed an enduring methodological battle between statistics and experiments, and pointed out that the difficulties of randomization and locating credible counterfactuals have served as main hurdles for IPE scholars to widely adopt experimental tools. However, I further demonstrated some new progress in applying survey, field, and lab experiments in the recent IPE scholarship. I concluded that it is crucial for future researchers to think innovatively about how to combine different research methods to make causal claims in IPE studies.

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.

R Bootcamp: Fall 2021

August 21, 2021, 8:30am
The workshop will be an intensive two-day introduction to R using RStudio. After the first morning session, the workshop will (staffing permitting) be split into two separate tracks. Co-sponsored by the UC Berkeley Statistics Department and the D-Lab.

Introduction to Propensity Score Matching with MatchIt

April 1, 2024
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
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.

Design Your Observational Study with the Joint Variable Importance Plot

March 12, 2024
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.