Latent class (LC) analysis is a widely used method for extracting meaningful groups (LCs) from data. The basic concept was introduced by Paul Lazarsfeld in 1950 for building typologies (or clusters) from dichotomous variables as part of his more general latent structure analysis. In 1974, Leo Goodman operationalized and extended LC analysis using maximum likelihood methods, which resolved previous implementation problems. After 1995, extensions to traditional LC modeling took place, and Jeroen K. Vermunt and Jay Magidson defined the LC model more generally as any model where some parameters differ across unobserved subgroups called LCs. LC modeling has since become a general multivariate modeling approach for revealing latent segments based on any set of observed indicators, across a wide range of applications. In particular, LC generalizes traditional cluster, factor, and item response theory analyses and also generalizes various kinds of regression modeling where the parameters that differ for different classes are the regression coefficients. This entry discusses traditional LC modeling, tools for determining the number of classes, approaches for identifying meaningful classes, and advanced LC regression models for analyzing ratings and choice data. Several other topics—such as dealing with covariates using one-step and three-step approaches, multilevel LC models, latent Markov models, and LC growth models—are also briefly discussed.