Exploratory Factor Analysis with Jamovi

Uncovering underlying patterns in complex survey responses and multidimensional datasets is a foundational skill in data analysis and behavioral research. This text-based course guides you through conducting, evaluating, and interpreting Exploratory Factor Analysis (EFA) using Jamovi. You will read clear explanations that demystify statistical terminology before applying step-by-step analytical procedures. By working through realistic case scenarios and text walkthroughs, you will develop the confidence to validate measurement instruments and interpret underlying latent variables accurately. What you'll learn: - Understand fundamental concepts of latent variables, factor retention, and variance explanation - Assess dataset suitability using Bartlett's test of sphericity and Kaiser-Meyer-Olkin (KMO) measures - Apply appropriate extraction methods and rotation strategies, including orthogonal and oblique rotations - Interpret factor loading tables, communalities, and cross-loadings effectively - Evaluate internal consistency and scale reliability using Cronbach's alpha and McDonald's omega - Practice reporting and documenting EFA findings clearly for academic or professional research The course begins with essential statistical definitions and assumption checks before moving systematically into model estimation, factor extraction, rotation decisions, and reliability analysis. This course is designed for beginners, researchers, and data analysts looking for a practical, step-by-step introduction to dimensional reduction without needing prior advanced statistical expertise. Start reading today to build practical competence in factor analysis with Jamovi.