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How to Perform Exploratory Factor Analysis of Mixed Data using R

This video is a step by step guide for performing an exploratory factor analysis of mixed data using R. To perform Factor Analysis of Mixed Data (FAMD) in R, the FactoMineR package is an essential tool, enabling analysis of datasets with both continuous and categorical variables. Start by creating a mixed dataset with correlated variables using techniques like Gaussian copulas for controlling relationships. Once your data is ready, execute the FAMD() function, specifying the number of principal components (ncp) to extract. Analyze key outputs like cos2 (squared cosine for representation quality) and ctr (contribution to dimensions). Visualize results with individual and variable plots to interpret clusters and patterns effectively. FAMD in R simplifies exploring mixed data structures, making it invaluable for data science, machine learning, and market research applications. Factor Analysis of Mixed Data (FAMD) is a statistical technique ideal for analyzing datasets containing both continuous and categorical variables, providing insights into patterns and relationships within mixed data. Key metrics from FAMD, such as cos2 (quality of representation) and ctr (contribution), help interpret how well variables, categories, or individuals are explained by the dimensions (factors). Higher cos2 values indicate strong representation on a dimension, while ctr highlights the most influential variables. FAMD outputs include eigenvalues, variable and individual plots, and contributions, offering a comprehensive understanding of correlations and clusters in mixed datasets. This technique is widely used in market research, social science, and healthcare to uncover hidden insights in multidimensional data. The R codes used in this video are posted in the Comments.