Day 6: 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

=============

During this day, lavaan and semPlot packages are loaded. Then, an SEM model is defined. Following lines of codes can be used:

 

# Load packages

library(lavaan)

library(semPlot)

 

# Define the SEM model

model <- '

  # Direct effect

  retirement_planning ~ financial_literacy

 

  # Indirect effects

  financial_literacy ~ income_level

  savings_behavior ~ income_level + financial_literacy

  retirement_planning ~ savings_behavior

'

 

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

 

# Simulate example data

set.seed(123)

data <- data.frame(

  income_level = rnorm(100, mean = 50000, sd = 15000),  # Income level

  financial_literacy = rnorm(100, mean = 60, sd = 10),  # Financial literacy score

  savings_behavior = rnorm(100, mean = 5000, sd = 2000),  # Savings behavior

  retirement_planning = rnorm(100, mean = 70, sd = 15)  # Retirement planning score

)

 

The SEM model is fit in the data, and eventually the SEM model is visualized using the semPlot.

 

# Fit the SEM model

fit <- sem(model, data = data)

 

# View the summary of the model fit

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

Source:
ChatGPT