Exploratory Factor Analysis in R: A Practical Guide

Exploratory Factor Analysis in R: A Practical Guide

Key takeaways from this article EFA is an exploratory technique that tries to find the best factor model that fits the data without any prior assumptions or constraints. CFA is a confirmatory technique that tests whether a predefined factor model fits the data with some specified assumptions or constraints. EFA and CFA have different purposes and applications and can complement each other in factor analysis. To perform EFA and CFA in R, you need to use the  psych  and  lavaan  packages, which provide various functions for factor analysis and latent variable analysis. To interpret the results of EFA and CFA, you need to look at the factor loadings, factor scores, fit indices, and other statistics that indicate how well the factor model represents the data and what each factor means. Functions and their description used in this tutorial Function Description psych::fa.parallel() Performs parallel analysis and provides scree plots and other statistics for determining the number of factors to extract psy…