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.

Exercise C1.

Introduction to Latent Class Analysis Session 3: Some Advanced Topics C. Multilevel LC models Exercise C1.

• Download these files: mierlo_socmeth.txt and multilevel.lgf

• Open multilevel.lgf in LatentGOLD. Notice that the variable TEAM is specified as the group ID in the advanced tab, and the number of GClasses is specified as 1, 2 or 3 in the GClasses box. After estimating the models, compare the output for the standard 2-class model (model 2) containing only 1 GClass with the 2-class model which includes a 3-class structure on the groups (e.g., 3 GClasses).

• Compare the Gprofile output in model 2 with that in model 5. How do you interpret these parameters? Under model 5, what percentage of the 88 teams are in GClass 1? Are these teams more or less likely to be in latent class 1?

Note: For further technical details on the implementation of these models in LatentGOLD, see section 10 in the LatentGOLD Technical Guide (pages 19-24 of Session 3 Assigned Reading).

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