Higher Order Models
In plssem: Complex Partial Least Squares Structural Equation Modeling

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  eval = identical(Sys.getenv("IN_PKGDOWN"), "true")
)

library(plssem)

Higher Order Constructs

It is possible to estimate models with second order construcst with the pls() function, using the two-stage approach. Here we see an example using the TPB_2SO dataset, from the modsem package. The model below contains two second order latent variables, INT (intention) which is a second order latent variable of ATT (attitude) and SN (subjective norm), and PBC (perceived behavioural control) which is a second order latent variable of PC (perceived control) and PB (perceived behaviour).

library(modsem)

tpb_2so <- '
  # First order latent variables
  ATT =~ att1 + att2 + att3
  SN  =~ sn1 + sn2 + sn3
  PB =~ pb1 + pb2 + pb3
  PC =~ pc1 + pc2 + pc3
  BEH =~ b1 + b2

  # Higher order latent variables
  INT =~ ATT + SN
  PBC =~ PC + PB

  # Structural model
  BEH ~ PBC + INT + INT:PBC
'

fit <- pls(tpb_2so, data = TPB_2SO, bootstrap = TRUE, boot.R = 50)
summary(fit)