Education | EpiToDate

Self assessment
Case Study Scenarios
Practice with Dat

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

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

2 / 10

In the context of LCA, what does a high posterior probability for a specific class indicate about an individual’s classification?

The individual is highly likely to belong to that class based on their observed responses.

The individual has an equal chance of belonging to any class.

The individual’s observed variables do not conform well to any of the latent classes.

The model’s fit statistics need to be re-evaluated.

3 / 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

4 / 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.

5 / 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.

6 / 10

Which of the following would NOT typically be considered a problem for the validity of an LCA model?

Small sample size, reducing the power to detect distinct classes

Violation of the conditional independence assumption

Non-normality of observed variables

High correlation between observed variables within classes

7 / 10

Which of the following is an indicator that you may need more latent classes in your model?

High Bayesian Information Criterion (BIC) value

Low entropy score

Large p-values for chi-square goodness-of-fit tests

Low Akaike Information Criterion (AIC) value

8 / 10

If an LCA model with three classes has a BIC of 2500 and an LCA model with four classes has a BIC of 2490, what does this suggest?

The four-class model should be chosen because it has a slightly lower BIC.

The three-class model should be chosen because it has fewer parameters.

The four-class model is invalid because the BIC should increase with additional classes.

The difference in BIC is too small to justify adding a fourth class.

9 / 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.

10 / 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

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

Share this: