Latent-class analysis estimates unobserved categorical groups from patterns in observed categorical indicators. The classes are probabilistic and require checks for fit, stability, interpretation and external usefulness.
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Latent-class and mixture models
Models that represent unobserved heterogeneity by estimating distinct latent groups, states or response processes.
Methods family
Models that represent unobserved heterogeneity by estimating distinct latent groups, states or response processes.
Use methods only after defining the estimand, data structure, assumptions, validation plan and decision consequence.
Latent-profile analysis is a method within latent-class and mixture models. Models that represent unobserved heterogeneity by estimating distinct latent groups, states or response processes. Its usefulness depends on data structure, assumptions, validation and whether the output answers the intended decision.
Read the full entry →Latent-class regression is a method within latent-class and mixture models. Models that represent unobserved heterogeneity by estimating distinct latent groups, states or response processes. Its usefulness depends on data structure, assumptions, validation and whether the output answers the intended decision.
Read the full entry →Latent-class choice modelling is a method within latent-class and mixture models. Models that represent unobserved heterogeneity by estimating distinct latent groups, states or response processes. Its usefulness depends on data structure, assumptions, validation and whether the output answers the intended decision.
Read the full entry →Latent-transition analysis is a method within latent-class and mixture models. Models that represent unobserved heterogeneity by estimating distinct latent groups, states or response processes. Its usefulness depends on data structure, assumptions, validation and whether the output answers the intended decision.
Read the full entry →Growth-mixture modelling is a method within latent-class and mixture models. Models that represent unobserved heterogeneity by estimating distinct latent groups, states or response processes. Its usefulness depends on data structure, assumptions, validation and whether the output answers the intended decision.
Read the full entry →Finite-mixture models is a method within latent-class and mixture models. Models that represent unobserved heterogeneity by estimating distinct latent groups, states or response processes. Its usefulness depends on data structure, assumptions, validation and whether the output answers the intended decision.
Read the full entry →Mixture regression is a method within latent-class and mixture models. Models that represent unobserved heterogeneity by estimating distinct latent groups, states or response processes. Its usefulness depends on data structure, assumptions, validation and whether the output answers the intended decision.
Read the full entry →Zero-inflated mixture models is a method within latent-class and mixture models. Models that represent unobserved heterogeneity by estimating distinct latent groups, states or response processes. Its usefulness depends on data structure, assumptions, validation and whether the output answers the intended decision.
Bayesian latent-class models is a method within latent-class and mixture models. Models that represent unobserved heterogeneity by estimating distinct latent groups, states or response processes. Its usefulness depends on data structure, assumptions, validation and whether the output answers the intended decision.
Latent-class conjoint is a statistical or analytical concept within latent-class and mixture models. It should be selected for the data-generating process and decision question rather than because software makes it available.
Latent-class MaxDiff is a statistical or analytical concept within latent-class and mixture models. It should be selected for the data-generating process and decision question rather than because software makes it available.
Latent-class segmentation is a statistical or analytical concept within latent-class and mixture models. It should be selected for the data-generating process and decision question rather than because software makes it available.
Browse linked method entries
Latent-class analysis
Latent-class analysis estimates unobserved categorical groups from patterns in observed categorical indicators. The classes are probabilistic and require checks for fit, stability, interpretation and external usefulness.
Open methodLatent-profile analysis
Latent-profile analysis is a method within latent-class and mixture models. Models that represent unobserved heterogeneity by estimating distinct latent groups, states or response processes. Its usefulness depends on data structure, assumptions, validation and whether the output answers the intended decision.
Open methodLatent-class regression
Latent-class regression is a method within latent-class and mixture models. Models that represent unobserved heterogeneity by estimating distinct latent groups, states or response processes. Its usefulness depends on data structure, assumptions, validation and whether the output answers the intended decision.
Open methodLatent-class choice modelling
Latent-class choice modelling is a method within latent-class and mixture models. Models that represent unobserved heterogeneity by estimating distinct latent groups, states or response processes. Its usefulness depends on data structure, assumptions, validation and whether the output answers the intended decision.
Open methodLatent-transition analysis
Latent-transition analysis is a method within latent-class and mixture models. Models that represent unobserved heterogeneity by estimating distinct latent groups, states or response processes. Its usefulness depends on data structure, assumptions, validation and whether the output answers the intended decision.
Open methodGrowth-mixture modelling
Growth-mixture modelling is a method within latent-class and mixture models. Models that represent unobserved heterogeneity by estimating distinct latent groups, states or response processes. Its usefulness depends on data structure, assumptions, validation and whether the output answers the intended decision.
Open methodFinite-mixture models
Finite-mixture models is a method within latent-class and mixture models. Models that represent unobserved heterogeneity by estimating distinct latent groups, states or response processes. Its usefulness depends on data structure, assumptions, validation and whether the output answers the intended decision.
Open methodMixture regression
Mixture regression is a method within latent-class and mixture models. Models that represent unobserved heterogeneity by estimating distinct latent groups, states or response processes. Its usefulness depends on data structure, assumptions, validation and whether the output answers the intended decision.
Open methodZero-inflated mixture models
Zero-inflated mixture models is a method within latent-class and mixture models. Models that represent unobserved heterogeneity by estimating distinct latent groups, states or response processes. Its usefulness depends on data structure, assumptions, validation and whether the output answers the intended decision.
Open methodHidden Markov models
Hidden Markov models is a method within latent-class and mixture models. Models that represent unobserved heterogeneity by estimating distinct latent groups, states or response processes. Its usefulness depends on data structure, assumptions, validation and whether the output answers the intended decision.
Open methodHidden-class models
Hidden-class models is a method within latent-class and mixture models. Models that represent unobserved heterogeneity by estimating distinct latent groups, states or response processes. Its usefulness depends on data structure, assumptions, validation and whether the output answers the intended decision.
Open methodBayesian latent-class models
Bayesian latent-class models is a method within latent-class and mixture models. Models that represent unobserved heterogeneity by estimating distinct latent groups, states or response processes. Its usefulness depends on data structure, assumptions, validation and whether the output answers the intended decision.
Open methodLatent-class conjoint
Latent-class conjoint is a statistical or analytical concept within latent-class and mixture models. It should be selected for the data-generating process and decision question rather than because software makes it available.
Open methodLatent-class MaxDiff
Latent-class MaxDiff is a statistical or analytical concept within latent-class and mixture models. It should be selected for the data-generating process and decision question rather than because software makes it available.
Open methodLatent-class segmentation
Latent-class segmentation is a statistical or analytical concept within latent-class and mixture models. It should be selected for the data-generating process and decision question rather than because software makes it available.
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