methods

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methods

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")

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.