Learning Objectives

Welcome to Introduction to Research Methods, a course originally taught at the University of California, Santa Barbara, now accessible to everyone. This course serves as a gateway into the world of quantitative research in social sciences, especially suited for those with no formal training in statistics or programming. Whether you’re starting graduate school, looking to strengthen your grasp on quantitative methods, or simply interested in understanding scientific papers in fields like economics, political science, or public policy analysis, this course has something valuable for you.

Structured in concise, 20-minute video modules, the course offers flexibility and targeted learning. You can choose topics that interest you the most or engage with the entire series for a comprehensive understanding. Each module is designed to demystify statistical concepts like hypothesis testing, Type I and Type II errors, and more, in an accessible and efficient manner.

As an introduction to quantitative research, the course covers foundational topics such as probability, statistical inference, regression analysis, and the evaluation of causality in social sciences. This course goes beyond just learning methods or statistics; it’s about applying these skills in life and questioning common claims encountered in everyday news and publications.

In an age where we are inundated with a myriad of competing information sources, it equips you to be an informed consumer of news and information, enhancing your ability to assess and evaluate the validity of various claims. The lecture on causality and experiments, for example, is particularly insightful for examining headline-grabbing conclusions about the impact of public policies. This approach cultivates critical thinking skills, enabling you to think more deeply about evidence and distinguish between significant findings and mere background noise.

What sets this course apart is its practical approach to learning how to conduct data analysis, with a strong emphasis on coding in R, a leading statistical software. The course includes assignments for hands-on experience in data analysis, yet it is structured to also be valuable for those who prefer to focus on the theoretical aspects and practical insights without engaging in coding.

To complement the video modules, you’ll find a range of supporting materials including slides, handouts, and coding exercises complete with solutions. These resources are designed to reinforce your learning and help you gauge your understanding.

Originally delivered during the height of the pandemic, the course is fully remote and can be completed asynchronously, allowing for flexibility in your learning schedule.


Take a look at the syllabus for more information on the concepts covered by the course. It not only explains how the coding assignments function and offers guidance for beginning with R programming, but also provides tips and links to additional learning materials to enhance your coding skills.


Note. This syllabus was originally designed for an online course delivered in 2020 at the University of California. While it contains useful resources, please disregard dates, deadlines, and UC-only academic policies. The course is now available for the general public.



Bailey, Michael A. 2016. Real Stats: Using Econometrics for Political Science and Public Policy. Oxford University Press.


The course utilizes a selection of quantitative empirical research papers as practical examples, enabling students to learn hands-on how to analyze regression tables, interpret results, and delve into how data analysis is conducted in academic research.

❗ Due to copyright rules, I cannot post the readings here. However, if you are having trouble accessing the readings, feel free to email me so that I can assist you in obtaining the required materials.