The Latent Class Analysis platform fits a latent class model to categorical response variables and determines the most likely cluster or latent class for each observation. A latent variable is an unobservable grouping variable. Each level of the latent variable is called a latent class. For example, latent classes could be clusters of survey respondents that are grouped by their preference for risk.
The model takes the form of a multinomial mixture model. There are two sets of parameters in the model: the γ parameters and the ρ parameters. The γ parameters represent the overall probabilities of cluster membership. The ρ parameters represent the probabilities of observing a given response conditional on cluster membership. A latent class is characterized by a pattern of these conditional probabilities.
In order for the analysis results to be meaningful, a subject matter expert must interpret the clusters that the platform generates. This subject matter expert examines characteristics of the latent classes and constructs a definition for each class based on those characteristics.
Note: Rows with missing values in any of the response columns are excluded from the analysis.
For more information about latent class models, see Collins and Lanza (2010) and Goodman (1974).
Latent Class Analysis is one of four platforms that JMP provides for clustering observations. For a comparison of all four methods, see Overview of Platforms for Clustering Observations.