by William Revelle
My course in psychometric theory, on which much of this book is based, was inspired by a course of the same name by Warren Norman. The organizational structure of this text owes a great deal to the structure of Warren’s course. Warren introduced me, as well as a generation of graduate students at the University of Michigan, to the role of theory and measurement in the study of psychology.
by Burak AYDIN, James ALGINA, Walter LEITE, Hakan ATILGAN
We aim to create a platform for the applied social scientists in which we can demonstrate basic statistical procedures using R and real data. We prefer to name this material as a platform given that (a) it is open for contribution, (b) it will have dynamic content and (c) it can serve as a mainboard for Plug-ins and Add-ons .
by Kyle Walker
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.
by Paul C. Bauer, Camille Landesvatter, many others
The present online book provide a review of APIs that may be useful for social scientists. Covers a wide selection of APIs from google, Instagram, Youtube and others. R code included.
Complex Surveys is a practical guide to the analysis of survey data using R, the freely available and downloadable statistical programming language. As creator of the specific survey package for R, the author provides the ultimate presentation of how to successfully use the software for analyzing data from complex surveys while also utilizing the most current data from health and social sciences studies to demonstrate the application of survey research methods in these fields.
Composite indicators are aggregations of indicators which aim to measure (usually socio-economic) complex and multidimensional concepts which are difficult to define, and cannot be measured directly. Examples include innovation, human development, environmental performance, and so on. This book gives a detailed guide on building composite indicators in R, focusing on the recent COINr package, which is an end-to-end development environment for composite indicators. Although COINr is the main tool used in the book, it also gives general explanation and guidance on composite indicator construction and analysis in R, ranging from normalisation, aggregation, multivariate analysis and global sensitivity analysis.
by Wouter van Atteveldt, Damian Trilling, Carlos Arcila Calderon
Assuming little or no background in data science or computer linguistics, this accessible textbook teaches readers how to use state-of-the art computational methods to perform data-driven analyses of social science issues. A cross-disciplinary team of authors—with expertise in both the social sciences and computer science—explains how to gather and clean data, manage textual, audio-visual, and network data, conduct statistical and quantitative analysis, and interpret, summarize, and visualize the results.
by Paul C. Bauer
The goals for this course are twofold. First, I hope you will gain a solid understanding of how access to big data (digital traces) is changing the social sciences in terms of a) new substantial and theoretical insights, and in terms of b) new methodologies. Second, I hope you will learn which and how big data could be used to answer further pressing questions you might encounter in the future.
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
by Jacob Kaplan
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.
Course material with Youtube Video
by Chester Ismay, Albert Y. Kim, Hendrik Feddersen
The intention of this book is to encourage more ‘data driven’ decisions in HR. HR Analytics is not anymore a nice-to-have addon but rather the way HR practitioners should conduct HR decision making in the future. Where applicable, human judgement is ‘added’ onto a rigorous analysis of the data done in the first place.
To achieve this ideal world, I need to equip you with some fundamental knowledge of R and RStudio, which are open-source tools for data scientists. I am well aware that on one side you want to do something for your career in HR, however you are most likely completely new to coding.
by 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.
by 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.
by Matthew J. C. Crump
This is a series of labs/tutorials for a two-semester graduate-level statistics sequence in Psychology @ Brooklyn College of CUNY. The goal of these tutorials is to 1) develop a deeper conceptual understanding of the principles of statistical analysis and inference; and 2) develop practical skills for data-analysis, such as using the increasingly popular statistical software environment R to code reproducible analyses.
by 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.
This script will cover the pre-processing of text, the implementation of supervised and unsupervised approaches to text, and in the end, I will briefly touch upon word embeddings and how social science can use them for inquiry.
by Kieran Healy
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.
by Quan Li
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.
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