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Summarize the Multi-Factor Model

You have now created a two-factor model of the reading comprehension and speeded addition factors. Is that better than a one-factor model? Use the cfa() and summary() functions on your new two-factor model of the HolzingerSwineford1939 dataset to show the fit indices.

Diese Übung ist Teil des Kurses

Structural Equation Modeling with lavaan in R

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Anleitung zur Übung

  • Save your model fit as twofactor.fit using the cfa() function.
  • Use the summary() function to view the fitted model.
  • Compare the fit indices from the one-factor model to the updated model.

Interaktive Übung

Vervollständige den Beispielcode, um diese Übung erfolgreich abzuschließen.

# Previous one-factor model output
summary(text.fit, standardized = TRUE, fit.measures = TRUE)

# Two-factor model specification
twofactor.model <- 'text =~ x4 + x5 + x6
speed =~ x7 + x8 + x9'

# Use cfa() to analyze the model and include data argument


# Use summary() to view the fitted model
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