Explore the concept of Factor Analysis in R, a statistical technique used to understand unobservable variables that cannot be directly measured. This course will guide you through the process of conducting both exploratory and confirmatory factor analyses, allowing you to develop a model that aligns with your theories and data. Begin by delving into exploratory factor analysis (EFA), where you will learn how to visualize and interpret factor loadings, analyze factor scores, and test correlations. Once you have mastered single-factor EFA, you will progress to multidimensional data, exploring techniques such as calculating eigenvalues and creating screen plots. In the next phase, you will be introduced to confirmatory factor analysis (CFA), where you will learn how to generate syntax based on EFA results and theory. This will enable you to refine your model and measure, ensuring a more accurate representation of your constructs. The final chapter of this course compares EFAs and CFAs, providing examples of both and offering insights on how to enhance your models. By acquiring these statistical techniques, you will be equipped to develop, refine, and share your measures effectively. These analyses have broad applications across various fields, including psychology, education, political science, economics, and linguistics.