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Latent class analysis (LCA)
Discover and understand unobserved groups (latent classes)
in your data–whether the groups are consumers with different
buying preferences, healthy and unhealthy individuals, or teens
with high, medium, and low risk of high school drop out. You can
use LCA as a model-based method of classification. Or you can
fit SEM path models and test for differences across the
unobserved groups. Estimate the proportion of the population
in each group, estimate group means, and more.
Learn about latent class analysis.
Model types 
- Latent class models
- Latent profile models
- Finite mixture models
- Path models with categorical latent variables
- Multiple-group models with known groups
Categorical latent variables measured by
- Binary items
- Ordinal items
- Continuous items
- Count items
- Categorical items
- Fractional items
- Survival times
Model class membership
- Predictors of class membership
- Multinomial logistic model
Starting values
- EM algorithm
- Fixed or random starting values
- Select number of random draws
Constraints
- Easily specify equality constraints across classes
- Constrain one parameter
- Cross-class equality constraints—just type lcinvariant(cons)
to constrain intercepts
Multiple-group models
- Allow for differences in LCA across known groups
- Group estimation is as easy as group(agegroup)
- Some parameters constrained and others estimated freely across groups
Goodness of fit
- Likelihood-ratio test vs saturated model (G2 statistic)
- AIC
- BIC
Inferences
- Expected means, probabilities, or counts in each class
- Expected proportion of population in each class
- AIC and BIC information criteria
- Wald tests of linear and nonlinear constraints
- Likelihood-ratio tests
- Contrasts
- Pairwise comparisons
- Linear and nonlinear combinations of coefficients with SEs and CIs
Predictions
- Class membership
- Posterior class membership
- Predicted means, probabilities, counts
- For each latent class
- Marginal with respect to latent classes
- Marginal with respect to posterior latent classes
- Survivor function
- Density function
- Distribution function
Postestimation Selector
- View and run all postestimation features for your command
- Automatically updated as estimation commands are run
Factor variables
- Automatically create indicators based on categorical variables
- Form interactions among discrete and continuous variables
- Include polynomial terms
- Perform contrasts of categories/levels
Marginal analysis
- Estimated marginal means
- Marginal and partial effects
- Average marginal and partial effects
- Adjusted predictions, means, and effects
- Works with multiple outcomes simultaneously
- Contrasts of margins
- Pairwise comparisons of margins
- Profile plots
- Graphs of margins and marginal effects
Additional resources
See
tests, predictions, and effects.
See New in Stata 18 to learn about what was added in Stata 18.
