methods

In modsem: Latent Interaction (and Moderation) Analysis in Structural Equation Models (SEM)

EVAL_DEFAULT <- FALSE
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  eval = EVAL_DEFAULT
)

library(modsem)

There are a number of approaches for estimating interaction effects in SEM. In modsem(), the method = "method" argument allows you to choose which to use. Different approaches can be categorized into two groups: Product Indicator (PI) and Distribution Analytic (DA) approaches.

Product Indicator (PI) Approaches:

• "ca" = constrained approach (Algina & Moulder, 2001)
  • Note that constraints can become quite complicated for complex models, particularly when there is an interaction including enodgenous variables. The method can therefore be quite slow.
• "uca" = unconstrained approach (Marsh, 2004)
• "rca" = residual centering approach (Little et al., 2006)
• "dblcent" = double centering approach (Marsh., 2013)
• default
• "pind" = basic product indicator approach (not recommended)

Distribution Analytic (DA) Approaches

• "lms" = The Latent Moderated Structural equations (LMS) approach, see the vignette
• "qml" = The Quasi Maximum Likelihood (QML) approach, see the vignette
• "mplus"
• estimates model through Mplus, if it is installed

m1 <- '
# Outer Model
X =~ x1 + x2 + x3
Y =~ y1 + y2 + y3
Z =~ z1 + z2 + z3

# Inner model
Y ~ X + Z + X:Z 
'

# Product Indicator Approaches
modsem(m1, data = oneInt, method = "ca")
modsem(m1, data = oneInt, method = "uca")
modsem(m1, data = oneInt, method = "rca")
modsem(m1, data = oneInt, method = "dblcent")

# Distribution Analytic Approaches
modsem(m1, data = oneInt, method = "mplus")
modsem(m1, data = oneInt, method = "lms")
modsem(m1, data = oneInt, method = "qml")

modsem documentation built on April 3, 2025, 7:51 p.m.