Factor Analysis Vs Pca

Factor Analysis is a statistical method used to identify underlying relationships between variables by grouping them into factors. It aims to reduce the number of observed variables into fewer latent variables, allowing researchers to understand the structure of the data and uncover patterns that may not be immediately apparent. This technique is widely utilized in social sciences, marketing research, and psychology to simplify data interpretation and enhance the clarity of complex datasets.

Principal Component Analysis (PCA) is a dimensionality reduction technique that transforms a large set of variables into a smaller one while retaining as much information as possible. It achieves this by identifying the principal components, which are the directions of maximum variance in the data. PCA is commonly used in exploratory data analysis and for making predictive models, as it helps in visualizing high-dimensional data and mitigating issues related to multicollinearity in regression models.