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Self assessment

The self-assessment section, along with targeted quizzes across the site, provides an interactive way for you to test and deepen your understanding of core epidemiology concepts and reinforce critical principles.

Latent class analysis

Test your knowledge of the principles of latent class analysis

1 / 10

Which of the following methods can be used to evaluate model fit in Latent Class Analysis when BIC and AIC provide conflicting recommendations?

Bootstrap likelihood ratio test (BLRT)

Increasing sample size

Reducing the number of observed variables

Using a k-means clustering approach instead

2 / 10

How would you interpret a situation in which adding covariates to an LCA model changes the distribution of individuals across classes?

The covariates improve the model’s fit by altering the latent class structure.

The covariates likely explain variance in class membership but do not impact the class structure itself.

The addition of covariates has caused overfitting in the model.

The observed variables are not conditionally independent within each class.

3 / 10

What is one advantage of using Latent Class Analysis over traditional clustering methods like k-means clustering?

LCA can be used for continuous latent variables

LCA can handle categorical observed variables and provides probability-based class membership

LCA requires fewer parameters, making it simpler to implement

LCA automatically determines the number of classes based on data without needing model selection

4 / 10

In Latent Class Analysis, what does the conditional independence assumption imply?

Each observed variable is assumed to be independent of all other observed variables, regardless of class membership.

The observed variables are conditionally independent given the latent class membership.

The latent classes are independent of each other given the observed variables.

Observed variables have no influence on latent class membership.

5 / 10

If the entropy of an LCA model is low, what might this indicate about the model’s classification accuracy?

The model has high classification accuracy with distinct class boundaries.

The model’s classes are poorly defined, leading to lower confidence in class assignments.

The model has too many observed variables.

The model should include more latent classes to improve classification.

6 / 10

Which criterion is most often preferred for deciding on the number of classes in LCA models, especially when sample size is large?

Akaike Information Criterion (AIC)

Consistent Akaike Information Criterion (CAIC)

Bayesian Information Criterion (BIC)

Entropy

7 / 10

In an LCA model with covariates, how do covariates impact the interpretation of the latent classes?

They influence class membership probabilities but do not alter the interpretation of observed variables within classes.

They directly impact the relationship between observed variables and latent classes.

They eliminate the need for conditional independence within classes.

They allow for the merging of latent classes.

8 / 10

Which of the following assumptions is critical for conducting Latent Class Analysis?

Each observed variable is independent of the latent variable

Observed variables within a latent class are conditionally independent of each other

The latent classes are continuous rather than categorical

There is no assumption of conditional independence in LCA

9 / 10

In the context of LCA, what does entropy measure?

The degree of overlap between latent classes

The quality of classification, indicating how well individuals are assigned to latent classes

The accuracy of parameter estimates for observed variables

The overall fit of the model to the data

10 / 10

Which type of indicator variable is most appropriate for Latent Class Analysis?

Continuous variables with high variance

Ordinal or nominal categorical variables

Latent variables with underlying continuous distributions

Your score is

The average score is 40%

Case Study Scenarios

The following are real-life epidemiology case scenarios where you can make decisions at each stage of an investigation (e.g., identifying study designs, selecting data collection methods, interpreting results).

Practice with Data

Latent class analysis

https://stats.oarc.ucla.edu/sas/dae/latent-class-analysis/
https://www.latentclassanalysis.com/code-repository/

Self assessment

Case Study Scenarios

Practice with Data

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