Latent structureMultivariate analysis
Latent Class Analysis (LCA)
Latent class analysis identifies unobserved subgroups — latent classes — within a population by finding patterns of responses across a set of categorical observed indicators. It is the categorical-variable counterpart of cluster analysis, but grounded in an explicit probabilistic model, and is widely used in social, health, and behavioral sciences to discover typologies in survey or diagnostic data.
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Sources
- Goodman, L. A. (1974). Exploratory latent structure analysis using both identifiable and unidentifiable models. Biometrika, 61(2), 215–231. DOI: 10.1093/biomet/61.2.215 ↗
- Lazarsfeld, P. F. & Henry, N. W. (1968). Latent Structure Analysis. Houghton Mifflin. link ↗
Related methods
Referenced by
Bayesian Cluster AnalysisBayesian Conjoint AnalysisBayesian K-means clusteringBayesian Latent Class AnalysisBayesian Mixture ModelingBayesian Multiple Correspondence AnalysisBayesian Network with Measurement ErrorCluster AnalysisCognitive Diagnosis ModelExplainable Gaussian Mixture ModelLatent Profile AnalysisMixture ModelingMultidimensional ScalingOrdinal RegressionRobust Latent Class AnalysisRobust Latent Profile Analysis