A latent variable is a variable that cannot be directly observed or measured but is assumed to exist based on observable or measurable data. Latent variables are inferred indirectly through a mathematical model from other observable variables that can be directly observed or measured. They are used in many disciplines, including political science, demography, engineering, medicine, ecology, physics, machine learning/artificial intelligence, bioinformatics, chemometrics, natural language processing, management, psychology, and the social sciences.
Latent variable models relate a set of observable variables (also called manifest variables or indicators) to a set of latent variables. It is assumed that the responses on the indicators or manifest variables are the result of an individuals position on the latent variable(s), and that the manifest variables have nothing in common after controlling for the latent variable (local independence) . Different types of latent variable models can be grouped according to whether the manifest and latent variables are categorical or continuous.
Examples of latent variables include quality of life, business confidence, morale, happiness, and conservatism. Latent variables are frequently used in psychology and social sciences to understand complex relationships among variables such as personality traits, attitudes, and behaviors. They are also used to model complex relationships among variables such as income, education, and maximum amount that people are willing to pay for goods in economics and finance.
Approaches to inferring latent variables from data include using a single observed variable, multi-item scales, predictive models, dimension reduction techniques such as factor analysis, structural equation models, and mixture models.
