This repository contains a comprehensive PLS-SEM analysis performed on influencer data using R. The code takes you through a methodical workflow that begins with data preprocessing and summary, then moves on to building measurement models—both reflective and formative—and specifying structural relationships between key latent constructs. Advanced validation techniques such as bootstrapping, mediation, and moderation testing are used to ensure the robustness of the model estimates.
Although applied to influencer data here, the methodology is highly versatile. Similar models can be adapted across various sectors like marketing, healthcare, finance, and social sciences. For instance, businesses can leverage this approach to understand customer behavior and predict product adoption, while healthcare researchers might use it to model patient outcomes or evaluate treatment effectiveness. This kind of analysis helps reveal underlying factors driving complex phenomena, leading to more informed strategic decisions and targeted interventions.
The code is organized and thoroughly documented, offering a reproducible template that demonstrates how advanced statistical modeling can be applied to real-world data challenges. It serves as a blueprint for addressing a wide range of problems where uncovering hidden relationships can drive impactful insights and improvements.
Both R code file (RMD) and HTML (with output) was uploaded for better understanding.