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.