Using R for data analysis in social sciences : a research project-oriented approach / Quan Li.

1. Learn about R and write first toy programs 2. Get data ready: Import, Inspect, and prepare data 3. One-sample and difference-of-means tests 4. Covariance and correlation 5. Regression analysis 6. Regression diagnostics and sensitivity analysis 7. Replication of findings in published analyses 8. Appendix: A brief introduction to analyzing categorical data and finding more data

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.