Quantitative Analysis

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...

Python Web Scraping

June 26, 2024, 10:00am
In this workshop, we cover how to scrape data from the web using Python. Web scraping involves downloading a webpage's source code and sifting through the material to extract desired data.

Python Data Wrangling and Manipulation with Pandas

June 25, 2024, 10:00am
Pandas is a Python package that provides fast, flexible, and expressive data structures designed to make working with 'relational' or 'labeled' data both easy and intuitive. It enables doing practical, real world data analysis in Python. In this workshop, we'll work with example data and go through the various steps you might need to prepare data for analysis.

Enhancing Research Transparency Inspired by Grounded Theory

April 30, 2024
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
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
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.

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.

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.

Python Data Wrangling and Manipulation with Pandas

March 4, 2024, 10:00am
Pandas is a Python package that provides fast, flexible, and expressive data structures designed to make working with 'relational' or 'labeled' data both easy and intuitive. It enables doing practical, real world data analysis in Python. In this workshop, we'll work with example data and go through the various steps you might need to prepare data for analysis.

R Machine Learning with tidymodels: Parts 1-2

February 27, 2024, 10:00am
Machine learning often evokes images of Skynet, self-driving cars, and computerized homes. However, these ideas are less science fiction as they are tangible phenomena that are predicated on description, classification, prediction, and pattern recognition in data. During this two part workshop, we will discuss basic features of supervised machine learning algorithms including k-nearest neighbor, linear regression, decision tree, random forest, boosting, and ensembling using the tidymodels framework. To social scientists, such methods might be critical for investigating evolutionary relationships, global health patterns, voter turnout in local elections, or individual psychological diagnoses.