modsem: Estimate interaction effects in structural equation models...

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

Estimate interaction effects in structural equation models (SEMs)

Description

modsem() is a function for estimating interaction effects between latent variables in structural equation models (SEMs). Methods for estimating interaction effects in SEMs can basically be split into two frameworks: 1. Product Indicator-based approaches ("dblcent", "rca", "uca", "ca", "pind") 2. Distributionally based approaches ("lms", "qml").

For the product indicator-based approaches, modsem() is essentially a fancy wrapper for lavaan::sem() which generates the necessary syntax and variables for the estimation of models with latent product indicators.

The distributionally based approaches are implemented separately and are not estimated using lavaan::sem(), but rather using custom functions (largely written in C++ for performance reasons). For greater control, it is advised that you use one of the sub-functions (modsem_pi, modsem_da, modsem_mplus) directly, as passing additional arguments to them via modsem() can lead to unexpected behavior.

Usage

modsem(model.syntax = NULL, data = NULL, method = "dblcent", ...)

Arguments

model.syntax	lavaan syntax
data	dataframe
method	method to use: "rca" = residual centering approach (passed to lavaan), "uca" = unconstrained approach (passed to lavaan), "dblcent" = double centering approach (passed to lavaan), "pind" = prod ind approach, with no constraints or centering (passed to lavaan), "lms" = latent model structural equations (not passed to lavaan), "qml" = quasi maximum likelihood estimation of latent model structural equations (not passed to lavaan), "custom" = use parameters specified in the function call (passed to lavaan).
...	arguments passed to other functions depending on the method (see modsem_pi, modsem_da, and modsem_mplus)

Value

modsem object with class modsem_pi, modsem_da, or modsem_mplus

Examples

library(modsem)
# For more examples, check README and/or GitHub.
# One interaction
m1 <- '
  # Outer Model
  X =~ x1 + x2 +x3
  Y =~ y1 + y2 + y3
  Z =~ z1 + z2 + z3

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

# Double centering approach
est1 <- modsem(m1, oneInt)
summary(est1)

## Not run: 
# The Constrained Approach
est1_ca <- modsem(m1, oneInt, method = "ca")
summary(est1_ca)

# LMS approach
est1_lms <- modsem(m1, oneInt, method = "lms", EFIM.S=1000)
summary(est1_lms)

# QML approach
est1_qml <- modsem(m1, oneInt, method = "qml")
summary(est1_qml)

## End(Not run)

# Theory Of Planned Behavior
tpb <- '
# Outer Model (Based on Hagger et al., 2007)
  ATT =~ att1 + att2 + att3 + att4 + att5
  SN =~ sn1 + sn2
  PBC =~ pbc1 + pbc2 + pbc3
  INT =~ int1 + int2 + int3
  BEH =~ b1 + b2

# Inner Model (Based on Steinmetz et al., 2011)
  INT ~ ATT + SN + PBC
  BEH ~ INT + PBC
  BEH ~ INT:PBC
'

# Double centering approach
est_tpb <- modsem(tpb, data = TPB)
summary(est_tpb)

## Not run: 
# The Constrained Approach
est_tpb_ca <- modsem(tpb, data = TPB, method = "ca")
summary(est_tpb_ca)

# LMS approach
est_tpb_lms <- modsem(tpb, data = TPB, method = "lms")
summary(est_tpb_lms)

# QML approach
est_tpb_qml <- modsem(tpb, data = TPB, method = "qml")
summary(est_tpb_qml)

## End(Not run)

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