Books in the Elements in Quantitative and Computational Methods for the Social Sciences series

An accessible and practical guide for the analysis and interpretation of regression discontinuity (RD) designs. The focus is on the canonical sharp RD setup that has the following features: (i) the score is continuously distributed and has only one dimension, (ii) there is only one cutoff, and (iii) compliance with the treatment assignment is perfect.

Shows how innovation in computer vision methods can markedly lower the costs of using images as data. Introduces readers to deep learning algorithms commonly used for object recognition, facial recognition, and visual sentiment analysis. Provides guidance and instruction for scholars interested in using these methods in their own research.

Twitter presents an ideal combination of size, international reach, and data accessibility that make it a useful data source. Acquiring, cleaning, and analyzing these data, however, require new tools and processes. This Element introduces these methods and provides scripts and examples for downloading, processing, and analyzing Twitter data.

Nonresponse and other sources of bias are endemic features of public opinion surveys. We elaborate a general workflow of weighting-based survey inference, and describe in detail how this can be applied to the analysis of historical and contemporary opinion polls.

Text is a fantastic resource for social scientists, but because it is so abundant, and so variable, it can be difficult to extract the information we want. Many basic text analysis methods are available as Python implementations: this Element will teach you when to use which method, how it works, and the Python code to implement it.

This Element provides the working social scientist with an overview of the most common methods for text classification, an intuition of their applicability, and Python code to execute them. It covers both the ethical foundations of such work as well as the emerging potential of neural network methods.