23 Social Science
23.1 Analyzing US Census Data: Methods, Maps, and Models in R
Census data are widely used in the United States across numerous research and applied fields, including education, business, journalism, and many others. Until recently, the process of working with US Census data has required the use of a wide array of web interfaces and software platforms to prepare, map, and present data products. The goal of this book is to illustrate the utility of the R programming language for handling these tasks, allowing Census data users to manage their projects in a single computing environment.
23.2 Computing for the Social Sciences
The goal of this course is to teach you basic computational skills and provide you with the means to learn what you need to know for your own research. I start from the perspective that you want to analyze data, and programming is a means to that end. You will not become an expert programmer - that is a given. But you will learn the basic skills and techniques necessary to conduct computational social science, and gain the confidence necessary to learn new techniques as you encounter them in your research.
We will cover many different topics in this course, including:
- Elementary programming techniques (e.g. loops, conditional statements, functions)
- Writing reusable, interpretable code
- Problem-solving - debugging programs for errors
- Obtaining, importing, and munging data from a variety of sources
- Performing statistical analysis
- Visualizing information
- Creating interactive reports
- Generating reproducible research
23.3 Crime by the Numbers: A Criminologist’s Guide to R
This book introduces the programming language R and is meant for undergrads or graduate students studying criminology. R is a programming language that is well-suited to the type of work frequently done in criminology - taking messy data and turning it into useful information. While R is a useful tool for many fields of study, this book focuses on the skills criminologists should know and uses crime data for the example data sets.
23.4 Introduction to R for Social Scientists:A Tidy Programming Approach
Ryan Kennedy, Philip Waggoner
Introduction to R for Social Scientists: A Tidy Programming Approach introduces the Tidy approach to programming in R for social science research to help quantitative researchers develop a modern technical toolbox. The Tidy approach is built around consistent syntax, common grammar, and stacked code, which contribute to clear, efficient programming. The authors include hundreds of lines of code to demonstrate a suite of techniques for developing and debugging an efficient social science research workflow.
23.5 Public Policy Analytics: Code & Context for Data Science in Government
Ken Steif, Ph.D
The goal of this book is to make data science accessible to social scientists and City Planners, in particular. I hope to convince readers that one with strong domain expertise plus intermediate data skills can have a greater impact in government than the sharpest computer scientist who has never studied economics, sociology, public health, political science, criminology etc.
23.6 Social Data Science with R
Daniel Anderson, Brendan Cullen, Ouafaa Hmaddi
Here’s an intro about why R is great and the cool things you can do with it and new problems you can address.
23.7 The Plain Person’s Guide to Plain Text Social Science
As a beginning graduate student in the social sciences, what sort of software should you use to do your work?1 More importantly, what principles should guide your choices? I offer some general considerations and specific answers.
23.8 Using R for Data Analysis in Social Sciences: A Research Project-Oriented Approach
This book seeks to teach undergraduate and graduate students in social sciences how to use R to manage, visualize, and analyze data in order to answer substantive questions and replicate published findings. This book distinguishes itself from other introductory R or statistics books in three ways. First, targeting an audience rarely exposed to statistical programming, it adopts a minimalist approach and covers only the most important functions and skills in R that one will need for conducting reproducible research projects. Second, it emphasizes meeting the practical needs of students using R in research projects. Specifically, it teaches students how to import, inspect, and manage data; understand the logic of statistical inference; visualize data and findings via histograms, boxplots, scatterplots, and diagnostic plots; and analyze data using one-sample t-test, difference-of-means test, covariance, correlation, ordinary least squares (OLS) regression, and model assumption diagnostics. Third, it teaches students how to replicate the findings in published journal articles and diagnose model assumption violations.
Paid ~$40 and listing of library availability