Principal Component Analysis in Jamovi: A Practical Guide

High-dimensional data often complicates statistical analysis and obscures underlying patterns. Principal Component Analysis (PCA) provides a structured method to reduce variable count while retaining key information and variance. In this text-based course, you will build a solid understanding of dimensionality reduction and learn how to conduct, evaluate, and report PCA using Jamovi. What you'll learn: - Understand fundamental statistical terms, variance, covariance, and eigenvalues - Check data suitability and verify essential assumptions before running PCA - Execute complete PCA workflows step by step using Jamovi - Choose appropriate component retention criteria including scree plots and parallel analysis - Apply orthogonal and oblique rotation methods to improve factor clarity - Interpret component loadings and calculate component scores for further analysis - Summarize and document analytical results following standard reporting conventions. The course starts with foundational definitions and theoretical concepts before guiding you through practical data setups and interpretation in Jamovi. This course is ideal for beginners, researchers, and data analysts seeking an accessible, code-free approach to multivariate data reduction. Begin reading today to strengthen your statistical analysis skills.