Data Science for the Social Sciences

This textbook offers a step-by-step introduction to the fundamentals of data analysis. It begins with descriptive analyses, moves on to linear regression, and then covers more advanced and sophisticated statistical models. Using the freely available statistical software R, the analyses are implemented in a clear and practical manner to make the theoretical concepts more tangible. In addition, the author introduces Quarto Markdown a tool that significantly simplifies the technical aspects of writing seminar papers, bachelor's and master's theses. Exercises at the end of each chapter encourage readers to apply the material covered and dig deeper on their own. The book is primarily intended for students in the social sciences.

The English translation of this book, originally in German, was facilitated by artificial intelligence. The content was later revised by the author for accuracy.

Explains statistical formulas instead of proving them, making complex concepts more accessible to learners Uses the free and open-source statistical software R to perform analyses and enhance readers' conceptual understanding Goes beyond linear regression, covering more advanced and complex statistical models

Autorentext

Dr. Benjamin E. Schlegel is a postdoctoral researcher at the Department of Political Science Methods, University of Zurich (Switzerland). His teaching and research focus on statistical methods, data analysis, and computational tools in the social sciences.

Inhalt

1. Introduction.- Part 1: I. Univariate Statistics.- 2. Concepts, Analysis Unit and Scales.- 3. Indices.- 4. Univariate Statistics.- 5. Distributions, Graphics and Quarto Markdown.- 6. Probability Theory and Random Samples.- 7. Test Theory, Inferential Statistics and Hypotheses.- Part II: Bivariate Statistics.- 8. Correlations of two variables.- 9. Theory of Hypothesis Testing.- 10. Univariate Linear Regression.- Part III: Multivariate Statistics.- 11. Multiple Linear Regression.- 12. Transformations, Interaction and Mediation.- 13. Uncertainty.- 14. Causality.- 15. Maximum Likelihood.- Part IV: Advanced Models.- 16. Advanced Models.- 17. Time Series Analysis.- 18. Panel Analysis.- 19. Logistic Regression.- 20. Event Count Models.- 21. Ordinal Logistic Regression.- 22. Multinomial Logistic Regression.- 23. Writing a Scientific Paper.