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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 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

2 / 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

3 / 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

4 / 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

5 / 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

6 / 10

Which of the following best describes the role of posterior probabilities in Latent Class Analysis?

They indicate the likelihood that an observed variable belongs to a specific class.

They reflect the probability that a particular class exists in the population.

They represent the probability of an individual's membership in each latent class.

They measure the overall goodness-of-fit of the LCA model.

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 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

9 / 10

Which of the following would indicate that adding another latent class to an LCA model does not improve model fit?

An increase in the likelihood ratio

A decrease in the entropy of class membership

A minimal reduction in BIC or AIC compared to previous models

A significant increase in posterior probabilities

10 / 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.

Your score is

The average score is 0%

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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