Exploratory Factor Analysis in SPSS

Welcome to Exploratory Factor Analysis (EFA) Using IBM SPSS Statistics, a comprehensive course designed to help you understand and apply one of the most powerful multivariate statistical techniques for identifying the underlying structure of data. Whether you are a student, researcher, academic, or data analyst, this course provides the theoretical foundation and practical skills required to perform Exploratory Factor Analysis (EFA) using IBM SPSS Statistics.

The course begins with the fundamentals of factor analysis, explaining its purpose, assumptions, and applications in scale development, questionnaire validation, and construct measurement. You will learn how to assess data suitability using Kaiser-Meyer-Olkin (KMO) and Bartlett's Test of Sphericity, extract factors using appropriate methods, interpret eigenvalues and scree plots, apply factor rotation techniques, interpret factor loadings, and determine the optimal factor structure. Through step-by-step demonstrations in SPSS and practical examples, you will gain hands-on experience in conducting exploratory factor analysis, validating measurement instruments, and reporting results according to accepted academic and research standards.

By the end of this course, you will have the confidence to perform Exploratory Factor Analysis using IBM SPSS Statistics and apply it effectively in academic research, dissertations, theses, psychometrics, market research, and professional data analysis.