Using R for data analysis in social sciences: a research project-oriented approach

Statistical analysis is common in the social sciences, and among the more popular programs is R. This text provides a foundation for undergraduate and graduate students in the social sciences on how to use R to manage, visualise, and analyse data. The focus is on how to address substantive questions with data analysis and replicate published findings. The work 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, visualise data and findings via histograms, boxplots, scatterplots, and diagnostic plots, and analyse data using one-sample t-test, difference-of-means test, covariance, correlation, ordinary least squares regression, and model assumption diagnostics.

Using R for Data Analysis in Social Sciences offers a step-by-step guide on using free software R to conduct and reproduce published quantitative analysis. It walks readers through data preparation, visualization, analysis, and reporting. Test-used in undergraduate classes and student self-study, it is appropriate for any introductory course on R, data analysis, and empirical social-scientific research.