Introduction to R for Social Researchers is a practical, step-by-step course designed to help social researchers confidently use R for real research. The focus is not on programming for its own sake, but on building a solid, usable workflow for working with social science data.

The course includes over 6 hours of video content, combining clear explanations with live demonstrations in R and RStudio. It takes you from first contact with R through the core skills needed for everyday research: preparing and cleaning data, working efficiently with R, creating clear, publication-ready visualisations, and carrying out common statistical procedures used in social research. Throughout, the emphasis is on both how to do things in R and why they are done that way in applied research settings.

Rather than abstract or toy examples, the course is grounded in realistic, real-world data familiar to social researchers, including large survey datasets and complex sample designs. Each section is accompanied by hands-on practical exercises, with worked solutions, so you can actively practise the skills and check your understanding as you go.

In addition to the videos and practicals, the course provides access to a dedicated discussion forum, where you can ask questions, clarify concepts, and get help when applying the material to your own data. This support is designed to help you move beyond passive learning and build genuine confidence in using R independently.

This course is well suited for:

• PhD students and early-career researchers
• Social scientists transitioning to R from SPSS, Stata, or Excel
• Policy, education, and applied researchers who need a modern data workflow
• Anyone who wants a clear, structured foundation before moving on to more advanced methods

By the end of the course, you will be able to work comfortably in R, understand the logic of modern R workflows, and apply R effectively to your own social research projects, using tools and practices that scale beyond this course.

Understanding why and when relationships between variables occur is central to quantitative social science. This course on Mediation and Moderation Analysis using R provides a complete, hands-on introduction to the two frameworks that allow researchers to uncover both mechanisms (mediation) and contextual effects (moderation). You will learn how to identify, estimate, and interpret direct, indirect, and interaction effects using modern statistical tools and real-world data.

Mediation analysis focuses on how one variable influences another through an intermediate mechanism — for example, how education affects health through income or wellbeing. Moderation analysis examines when or for whom an effect is stronger or weaker — for instance, whether the relationship between stress and health depends on gender or social support. Together, these methods reveal the structure of causal processes and provide deeper insights than simple regression models.

The course begins with the conceptual foundations of mediation and moderation, then progresses to advanced applications including path analysis, structural equation modeling (SEM), longitudinal mediation, multi-group analysis, and causal mediation frameworks. You will learn to use R packages such as lavaan, mediation, and interactions to estimate, visualise, and interpret these models.

Each lesson combines short video lectures, illustrated slides, and clear explanations with practical exercises based on real wold data — ensuring that all examples reflect realistic social research. Practical sessions guide you step-by-step through model estimation, diagnostics, and interpretation. Every exercise includes downloadable datasets, solutions, and R scripts, allowing you to replicate analyses and learn by doing.

By the end of the course, you will:

• Understand the logic and assumptions of mediation and moderation analysis.
• Estimate and interpret interaction and mediation models using R.
• Learn how to estimate moderation with the Johnson–Neyman method.
• Build path models and SEMs to capture direct, indirect, and total effects.
• Extend analyses to longitudinal and causal mediation frameworks.
• Conduct and interpret sensitivity analyses to assess robustness.
• Visualise and communicate results effectively through plots and model diagrams.

This course is designed for researchers, students, and analysts in the social, behavioural, and health sciences who already have basic familiarity with regression analysis and want to deepen their methodological toolkit. Whether you are working with survey data, experiments, or observational studies, you will gain the skills to design stronger studies, interpret results rigorously, and understand the mechanisms that drive social and behavioural outcomes.

Join the course to build a robust understanding of mediation and moderation analysis using R, and learn how to apply these techniques to your own research using transparent, reproducible, and data-driven methods.

Testimonials

Alexandru Cernat is the best quantitative analysis teacher I have experienced. His courses are well thought out and this one is the exact same. I feel my knowledge on mediation analysis has grown substantially, and the practicals accompanying the lectures are really useful.