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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

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.

2 / 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

3 / 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

4 / 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

5 / 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

6 / 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

7 / 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.

8 / 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.

9 / 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

10 / 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.

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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