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. To deepen the dedication to teaching Tidy best practices for conducting social science research in R, the authors include numerous examples using real world data including the American National Election Study and the World Indicators Data. While no prior experience in R is assumed, readers are expected to be acquainted with common social science research designs and terminology. Whether used as a reference manual or read from cover to cover, readers will be equipped with a deeper understanding of R and the Tidyverse, as well as a framework for how best to leverage these powerful tools to write tidy, efficient code for solving problems. To this end, the authors provide many suggestions for additional readings and tools to build on the concepts covered. They use all covered techniques in their own work as scholars and practitioners.

Ideal for introductory statistics courses at both the undergraduate and graduate levels, Basic Statistics for the Behavioral and Social Sciences Using R is specifically designed to make adoption simple in a variety of disciplines. The text includes topics typically covered in introductory textbooks: probability, descriptive statistics, visualization, comparisons of means, tests of association, correlations, OLS regression, and power analysis. However, it also transcends other books at this level by covering topics such as bootstrapping and an introduction to R, for those who are novices to this powerful tool. In a straightforward and easy-to-understand format, the authors provide readers with a plethora of freely available and robust resources and examples that are applicable to a wide variety of behavioral and social science disciplines, including social work, psychology, and physical and occupational therapy. The book is a must-read for all professors and students endeavoring to learn basic statistics.

Relevant, engaging, and packed with student-focused learning features, this book provides the basic step-by-step introduction to quantitative research and data every student needs.

Statistical analysis is common in the social sciences, and among the more popular programs is R. This book provides a foundation for undergraduate and graduate students in the social sciences on how to use R to manage, visualize, and analyze data. The focus is on how to address substantive questions with data analysis and replicate published findings. Using R for Data Analysis in Social Sciences adopts a minimalist approach and covers only the most important functions and skills in R to conduct reproducible research. It emphasizes the practical needs of students using R by showing 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. It also demonstrates how to replicate the findings in published journal articles and diagnose model assumption violations. Because the book integrates R programming, the logic and steps of statistical inference, and the process of empirical social scientific research in a highly accessible and structured fashion, it is appropriate for any introductory course on R, data analysis, and empirical social-scientific research.

This book outlines Bayesian statistical analysis in great detail, from the development of a model through the process of making statistical inference. The key feature of this book is that it covers models that are most commonly used in social science research - including the linear regression model, generalized linear models, hierarchical models, and multivariate regression models - and it thoroughly develops each real-data example in painstaking detail.

This unique text provides a step-by-step beginner’s guide to applying the Rasch model in R, a probabilistic model used by researchers across the social sciences to measure unobservable ("latent") variables. Each chapter is devoted to one popular Rasch model, ranging from the least to the most complex. Through a freely available and user-friendly package, BlueSky Statistics, Lamprianou offers a range of options for presenting results, critically examines the strengths and weaknesses of applying the Rasch model in each instance, and suggests more effective methodologies where applicable. With a focus on simple software code which does not assume extensive mathematical knowledge, the reader is initially introduced to the so-called simple Rasch Model to construct a "political activism" variable out of a group of dichotomously scored questions. In subsequent chapters, the book covers everything from the Rating Scale to the Many-facets Rasch model. The final chapter even showcases a complete mock manuscript, demonstrating how a Rasch-based paper on the identification of online hate speech should look like. Combining theoretical rigor and real-world examples with empirical datasets from published papers, this book is essential reading for students and researchers alike who aspire to use Rasch models in their research.

Quantitative social science research has been expanding due to the ava- ability of computers and data over the past few decades. Yet the textbooks and supplements for researchers do not adequately highlight the revolution created by the R software [2] and graphics system. R is fast becoming the l- gua franca of quantitative research with some 2000 free specialized packages, where the latest versions can be downloaded in seconds. Many packages such as “car” [1] developed by social scientists are popular among all scientists. An early 2009 article [3] in the New York Times notes that statisticians, engineers and scientists without computer programming skills ?nd R “easy to use.” A common language R can readily promote deeper mutual respect and understanding of unique problems facing quantitative work in various social sciences. Often the solutions developed in one ?eld can be extended and used in many ?elds. This book promotes just such exchange of ideas across many social sciences. Since Springer has played a leadership role in promoting R, we are fortunate to have Springer publish this book. A Conference on Quantitative Social Science Research Using R was held in New York City at the Lincoln Center campus of Fordham University, June 18–19, 2009. This book contains selected papers presented at the conference, representing the “Proceedings” of the conference.

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This book is the first compilation of its kind that brings together discussions of the evolution of scholarship in different branches of the Social Sciences. It presents a comprehensive multi-disciplinary text exploring the changing dynamics of the Social Sciences in Ghana, offering a broader perspective from which to view the evolution, theory, methods, substance and relevance of each of the Social Science disciplines and their multiple interfaces. The introduction and the conclusion are devoted to the theoretical, comparative and empirical debate over the Social Sciences from historical and analytical perspectives. Written by acknowledged experts, the 15 chapters span the following disciplines: Archaeology and Heritage Studies; History; Geography; Psychology; Sociology and Anthropology; Social Work; Economics; Political Science; International Affairs; Information Studies; Communication Studies; African Studies; Development Studies; Women’s and Gender Studies and Adult and Continuing Education. Changing Perspectives on the Social Sciences in Ghana offers sophisticated perspectives for comparing and appreciating the synergies, differences, trends and nuances among and between the Social Science disciplines in Ghana, in a holistic and scholarly manner.

Equips social scientists with the tools and techniques to conduct quantitative research in the age of big data.