Quadratic effects are essentially a special case of interaction effectsâwhere a variable interacts with itself. As such, all of the methods in modsem can also be used to estimate quadratic effects.
Below is a simple example using the LMS approach.
library(modsem)
m1 <- '
# Outer Model
X =~ x1 + x2 + x3
Y =~ y1 + y2 + y3
Z =~ z1 + z2 + z3
# Inner model
Y ~ X + Z + Z:X + X:X
'
est1Lms <- modsem(m1, data = oneInt, method = "lms")
summary(est1Lms)In this example, we have a simple model with two quadratic effects and one interaction effect. We estimate the model using both the QML and double-centering approaches, with data from a subset of the PISA 2006 dataset.
m2 <- '
ENJ =~ enjoy1 + enjoy2 + enjoy3 + enjoy4 + enjoy5
CAREER =~ career1 + career2 + career3 + career4
SC =~ academic1 + academic2 + academic3 + academic4 + academic5 + academic6
CAREER ~ ENJ + SC + ENJ:ENJ + SC:SC + ENJ:SC
'
est2Dblcent <- modsem(m2, data = jordan)
est2Qml <- modsem(m2, data = jordan, method = "qml")
summary(est2Qml)NOTE: The other approaches (e.g., LMS and constrained methods) can also be used but may be slower depending on the number of interaction effects, especially for the LMS and constrained approach.
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