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Fix the Latent Heywood Model

In the last exercise, you found that the adoption survey had a correlation > 1 on the latent variable. You should fix the Heywood case by collapsing the two latent variables into one latent variable. Then you should analyze and summarize the model to explore if merging these two factors into one has solved the problematic correlation.

Cet exercice fait partie du cours

Structural Equation Modeling with lavaan in R

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Instructions

  • Change the model to create only one goodstory factor that is measured by all six manifest variables in the adoptsurvey dataset.
  • Analyze the model with the cfa() function to ensure there are no error messages.
  • Run the summary() for the model to view the final model fit.

Exercice interactif pratique

Essayez cet exercice en complétant cet exemple de code.

# Look at the data
head(adoptsurvey)

# Edit the original model 
adopt.model <- 'goodstory =~ pictures + background + loveskids
inperson =~ energy + wagstail + playful'

# Analyze the model
adopt.fit <- cfa(model = ___, data = adoptsurvey)

# Look for Heywood cases
summary(___, standardized = TRUE, fit.measures = TRUE)
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