Generalized latent variable modeling

Generalized latent variable modeling is a statistical approach used to understand complex relationships by introducing underlying, unobserved factors—called latent variables— that influence observed data. These models help identify hidden patterns or traits, like personality or ability, that aren't directly measurable but affect observed responses. They are flexible, accommodating various data types (e.g., continuous, binary) and can analyze multiple variables simultaneously. This approach improves our understanding of the underlying structure in data, making it useful in fields like psychology, medicine, and social sciences for better insights and decision-making.