CFA vs EFA: Understand the Key Differences

CFA vs EFA: Understand the Key Differences

Key Points Factor analysis is a family of statistical methods that help you discover the underlying dimensions or factors that give rise to your observed data. EFA and CFA are two main types of factor analysis with different goals, assumptions, and procedures. EFA allows you to explore the possible structure of your data without prior assumptions, while CFA will enable you to test whether your data fit a predefined or hypothesized structure. The choice between EFA and CFA depends on your research question, data, and analysis level. EFA and CFA can be applied to various research fields, such as personality, job satisfaction, education, health, etc. Hello, this is Zubair Goraya , a data analyst and writer for Data Analysis, a website that provides tutorials on statistical data analysis. In this article, I will explain the difference between confirmatory factor analysis and exploratory factor analysis, two popular methods for studying data structure. I will also provide some examples, tips, an…