Research Project

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?

A Basic Introduction to Hierarchical Linear Modeling

March 4, 2024
Mingfeng Xue
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.

What Are Vowels Made Of? Graphing a Classic Dataset with R

February 13, 2024
Anna Björklund
by Anna Björklund. Vowels are all around us. Mainstream US English has around twelve unique vowels. How can our brains tell these sounds apart? This blog post will help you answer this question by plotting vowel data from a classic American English dataset by Peterson and Barney (1952).

How can we use big data from iNaturalist to address important questions in Entomology?

February 26, 2024
Leah Lee
by Leah Lee. Large-scale geographic data over time on insect diversity can be used to answer important questions in Entomology. Open-source, open-access citizen science platforms like iNaturalist generate huge amounts of data on species diversity and distribution at accelerating rates. However, unstructured citizen science data contain inherent biases and need to be used with care. One of the efforts to validate big data from iNaturalist is to cross-check with systematically collected data, such as museum specimens.

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.

Tracking Urban Expansion Through Satellite Imagery

December 12, 2023
Leïla Njee Bugha
by Leïla Njee Bugha. Among its many uses, remote sensing can prove especially useful to document changes and trends from eras or settings, where traditional sources are either inexistent or infrequently collected. This is the case when one wants to study urban expansion in sub-Saharan countries over the past 20 years. To further remedy the lack of data on land cover uses from earlier time periods, classification methods can be used as well. Using easily accessible satellite imagery from Google Earth Engine, I provide here an example combining remote sensing with classification to detect changes in the land cover in Nigeria since 2000 due to urban expansion.

From Ideas to Streamlined Research: The Benefits of Full-Cycle Methodology

December 5, 2023
Farnam Mohebi
by Farnam Mohebi. As an aspiring leading researcher, I find the full-cycle research methodology crucial for transforming initial curiosities into organized studies and research products. This approach begins with thorough observation, leads to theory and hypothesis development and experimentation, and concludes with synthesizing findings into coherent narratives. It's beneficial for researchers of all backgrounds, enhancing the depth and impact of their work. By embracing this method, researchers comprehensively understand each stage and its contribution to the broader research context and can lead the process of converting an initial unspecified research idea to a streamlined research study and product. This systematic approach is particularly effective in complex studies, fostering thorough, investigative, and innovative research processes.

Processing Videos in Python with OpenCV

November 28, 2023
Leah Lee
by Leah Lee. Videos and images are quickly becoming the most common type of data we store and interact with. Computer vision technologies derive useful information from these forms of data and are now commonly used in health care, agriculture, transportation, and security. OpenCV is a powerful tool for image processing and computer vision tasks. In this blog post, we will explore how we can use OpenCV in Python to carry out basic computer vision tasks. Specifically, we’ll focus on the simple task of identifying an object from a video and labeling a frame with a box around the object.

Searching for Other Solar Systems

November 21, 2023
Emma Turtelboom
by Emma Turtelboom. Over the last three decades, we have discovered over 5000 exoplanets, which are planets outside of our Solar System. With these observations, we can try to answer many questions we have about the universe. For example, how unique is the Solar System? How do planets form? Is there life elsewhere in the Milky Way? We can query the NASA Exoplanet Archive to compare multi-planet systems to the Solar System. Through this, we can compare how similar (or dissimilar!) the systems are.

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.