Introduction to Latent Class Analysis

Session 1: Latent Class Cluster Models
12 Topics | 13 Exercises
A. Basic Ideas of Latent Class Analysis
B. The general probability model for categorical variables
Exercise B.
C. Determining the number of classes/clusters
Exercise C.
D. Fit measures, model specification and selection strategies
Exercise D.
E. Classifying cases into latent class segments
Exercise E.
F. Interpreting LatentGOLD output
Exercise F1.
Exercise F2.
Exercise F3.
G. Example from survey analysis
Exercise G.
H. Including covariates in LC models
Exercise H.
I. Boundary, identification and local solution issues
Exercise I.
J. Extension to continuous variables and other scale types
Exercise J.
K. Including direct effects
Exercise K.
L. Examples with Diabetes data (obtaining scoring equations)
Exercise L.
Session 2: Latent Class Regression Models
3 Topics | 4 Exercises
A. Estimating a Simple LC Regression Model
Exercise A.
B. LC Regression Models with Predictors
C. LC Regression Models with repeated measurements
Exercise C1.
Exercise C2. Example: Latent class growth models
Exercise C3.
Session 4: End of Course Notes
Session 3: Some Advanced Topics
6 Topics | 6 Exercises
A. Incorporating/ accommodating cases with known class membership
Exercise A.
B. Latent Markov modeling for longitudinal data analysis
Exercise B1.
Exercise B2.
C. Multilevel LC models
Exercise C1.
D. Continuous factors (CFactor) and individual-level parameters
E. Controlling for the ‘level’ effect: specifying a random intercept model as an alternative to ‘centering’
Exercise E1.
F. Controlling for the ‘scale’ effect: using scale classes and scale CFactors
Exercise F1.
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E. Controlling for the ‘level’ effect: specifying a random intercept model as an alternative to ‘centering’

Introduction to Latent Class Analysis Session 3: Some Advanced Topics E. Controlling for the ‘level’ effect: specifying a random intercept model as an alternative to ‘centering’

Course Text: 29-31

Frequently, treating heterogeneity in the intercept as continuous works better than treating it as discrete. Such random intercept models (and other random effects models which allow for continuous heterogeneity) can be estimated in conjunction with LC models by adding up to 3 continuous factors (CFactors) in a model using the Advanced Module in LatentGOLD. See Exercise E1 for an example.

Exercises
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