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Self assessment
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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

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.

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

Why might an LCA researcher use bootstrapping when estimating model parameters?

To generate multiple random samples and select the best-fitting model

To handle violations of the conditional independence assumption

To obtain robust standard errors and confidence intervals for parameter estimates

To directly compare the fit of models with different numbers of classes

4 / 10

Which of the following scenarios would suggest that Latent Class Analysis might not be appropriate?

The observed variables are categorical.

There are strong correlations between observed variables within classes.

The sample size is small, limiting the power to detect distinct classes.

The latent structure is complex and may require more than four classes.

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 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

7 / 10

Which of the following is a primary method for determining the optimal number of latent classes in Latent Class Analysis?

Using K-means clustering

Assessing the stability of latent classes across different datasets

Comparing fit statistics like BIC (Bayesian Information Criterion) or AIC (Akaike Information Criterion)

Testing for statistical significance of each latent class using p-values

8 / 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.

9 / 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.

10 / 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

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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