### Accompanying Code for:
## Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R - A Workbook (2021)
## Hair, J.F. (Jr), Hult, T.M., Ringle, C.M., Sarstedt, M., Danks, N.P., and Ray, S.
## Chapter 4: Evaluation of reflective measurement models
# Load the SEMinR library
library(seminr)
# Load the data ----
corp_rep_data <- corp_rep_data
# Create measurement model ----
corp_rep_mm <- constructs(
composite("COMP", multi_items("comp_", 1:3)),
composite("LIKE", multi_items("like_", 1:3)),
composite("CUSA", single_item("cusa")),
composite("CUSL", multi_items("cusl_", 1:3)))
# Create structural model ----
corp_rep_sm <- relationships(
paths(from = c("COMP", "LIKE"), to = c("CUSA", "CUSL")),
paths(from = c("CUSA"), to = c("CUSL")))
# Estimate the model
corp_rep_pls_model <- estimate_pls(
data = corp_rep_data,
measurement_model = corp_rep_mm,
structural_model = corp_rep_sm,
missing = mean_replacement,
missing_value = "-99")
# Summarize the model results
summary_corp_rep <- summary(corp_rep_pls_model)
# Inspect iterations
summary_corp_rep$iterations
# Inspect the outer loadings
summary_corp_rep$loadings
# Inspect the indicator reliability
summary_corp_rep$loadings^2
# Inspect the internal consistency and reliability
summary_corp_rep$reliability
# Plot the reliabilities of constructs
plot(summary_corp_rep$reliability)
# Table of the FL criteria
summary_corp_rep$validity$fl_criteria
# HTMT Ratio
summary_corp_rep$validity$htmt
# Bootstrap the model
boot_corp_rep <- bootstrap_model(seminr_model = corp_rep_pls_model,
nboot = 1000)
# Store the summary of the bootstrapped model
sum_boot_corp_rep <- summary(boot_corp_rep, alpha = 0.10)
# Extract the bootstrapped HTMT
sum_boot_corp_rep$bootstrapped_HTMT
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