Visualization

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.

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.

by Mingfeng Xue. Measurements (e.g., tests, surveys, questionnaires) are inevitably involved with various sources of errors. Among many psychometric theories, item response theory stands out for its capability of detailed analyses at the item level and its potential to reduce some of the measurement errors. This post first discussed the limitations of conventional summation and average, which give rise to the IRT models, and then introduced a basic form of the Rasch model, including expressions of the model, the assumptions underlying it, some of its advantages, and software packages. Some codes are also provided.

by Mingyu Yuan. This blog post is the second part of “Twitter Text Analysis”. The goal is to use language models such as BERT to build a classifier on tweets. Word embedding, training and test splitting, model implementation, and model evaluation are introduced in this model.