Day 5: A challenge to learn basics of Structural Equation Modeling (SEM) using lavaan and semPlot packages in R

During the next 12 days, I will learn and repeat the basics of structural equation modeling (SEM) using lavaan and semPlot packages in R.

You can search my lavaan posts by typing: #UsmanZafarParacha_lavaan , and semPlot posts by typing: #UsmanZafarParacha_semPlot

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During this day, I loaded lavaan and semPlot packages. Then, defined the model using the following lines of codes:

 

 

# Define the SEM model

model <- '

  # Direct effects

  achievement ~ teaching_quality + peer_support + engagement

 

  # Indirect effects

  engagement ~ peer_support

 

  # Covariances (optional if you want to assess relationships between predictors)

  peer_support ~~ teaching_quality

'

 

Then, created a supposed data using the following lines of codes:

 

# Simulate data (for the sake of demonstration)

set.seed(123)

n <- 200

peer_support <- rnorm(n, mean = 5, sd = 2)

teaching_quality <- rnorm(n, mean = 6, sd = 1.5)

engagement <- 0.5 * peer_support + rnorm(n, mean = 3, sd = 1)

achievement <- 0.6 * teaching_quality + 0.3 * peer_support + 0.4 * engagement + rnorm(n)

 

data <- data.frame(peer_support, teaching_quality, engagement, achievement)

 

Then, I fit the SEM model to the data:

 

# Fit the model

fit <- sem(model, data = data)

# Get a summary of the model

summary(fit, fit.measures = TRUE, standardized = TRUE)

 

Then, the SEM model is visualized using the following lines of codes:

# Plot the SEM model

semPaths(fit, what = "std", layout = "tree", edge.label.cex = 1.2,

         nCharNodes = 0, color = list(lat = "blue", man = "green"),

         sizeMan = 6, sizeLat = 8)

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