SEMinR Lecture Series - Analyzing Categorical Predictors

SEMinR Lecture Series - Categorical Predictor variables

SEMinR Lecture Series

This session is focused on how to perform moderation analysis using SEMinR in Cran R.

Categorical Predictor Variables using SEMinR in R

  • The objective is to assess whether Country has an impact on Customer Loyalty.
  • Country is a categorical variable in the study with three countries China, Pakistan, and Italy.
  • Country is not added into the model directly. Since, it is a categorical variable, first the variable is dummy coded. Each Country will become a separate variable.
  • In this case Two categories (Countries) will be added in the model estimation. Whereas the third country will serve as a reference category.

Create Dummy Variables in R

  • Dummy variables are created when the exogenous variable is categorical in nature.
  • Each category is transformed into a dummy variable.
  • To create dummy variables in R, install fastDummies package

Video Tutorial

Complete Code

# Load the Data
datas <- read.csv(file = "Data.csv", header = TRUE, sep = ",")
# Create dummy variables
datas <- dummy_cols(datas, select_columns = "Country")
# Create measurement model
simple_mm <- constructs(
  composite("Loyalty", multi_items("CL", 1:6)),
  composite("Pakistan", single_item("Country_2")),
  composite("Italy", single_item("Country_3")))
# Create structural model
simple_sm <- relationships(
  paths(from = c("Pakistan", "Italy"), to = "Loyalty"))
# Estimate the model
simple_model <- estimate_pls(data = datas,
 measurement_model = simple_mm,
 structural_model = simple_sm,
 missing = mean_replacement,
 missing_value = "-99")
# Summarize the model results
summary_simple <- summary(simple_model)
# Bootstrap the model on the PLS Estimated Model
boot_model <- bootstrap_model(
  seminr_model = simple_model,
  nboot = 5000,
  cores = parallel::detectCores(),
  seed = 123)
# Store the summary of the bootstrapped model
# alpha sets the specified level for significance, i.e. 0.05
# Inspect the bootstrapped structural paths


Hair Jr, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., Danks, N. P., & Ray, S. (2021). Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R: A Workbook.

Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R

The tutorials on SEMinR are based on the mentioned book. The book is open source and available for download under this link.

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