IniziaInizia gratis

Explore Fit Indices

After reviewing the standardized loadings in the previous exercise, we found that several of the manifest variables may not represent our latent variable well. As a second measure of our model, you can examine the fit indices to see if the model appropriately fits the data. You can look at both the goodness of fit and badness of fit statistics using the fit.measures argument within the summary() function.

Remember that goodness of fit statistics, like the CFI and TLI, should be large (over .90) and close to one, while badness of fit measures like the RMSEA and SRMR should be small (less than .10) and close to zero.

Questo esercizio fa parte del corso

Structural Equation Modeling with lavaan in R

Visualizza il corso

Istruzioni dell'esercizio

  • Use the summary() function on your text.fit model.
  • Include the argument to view the fit indices.
  • Do not include the standardized loadings.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Load the lavaan library
library(lavaan)

# Load the data and define model
data(HolzingerSwineford1939)
text.model <- 'textspeed =~ x4 + x5 + x6 + x7 + x8 + x9'

# Analyze the model with cfa()
text.fit <- cfa(model = text.model, data = HolzingerSwineford1939)

# Summarize the model
Modifica ed esegui il codice