modsem introduces a new feature to the lavaan syntaxâthe semicolon operator (:). The semicolon operator works the same way as in the lm() function. To specify an interaction effect between two variables, you join them by Var1:Var2.
Models can be estimated using one of the product indicator approaches ("ca", "rca", "dblcent", "pind") or by using the latent moderated structural equations approach ("lms") or the quasi maximum likelihood approach ("qml"). The product indicator approaches are estimated via lavaan, while the lms and qml approaches are estimated via modsem itself.
Here is a simple example of how to specify an interaction effect between two latent variables in lavaan.
m1 <- '
# Outer Model
X =~ x1 + x2 + x3
Y =~ y1 + y2 + y3
Z =~ z1 + z2 + z3
# Inner Model
Y ~ X + Z + X:Z
'
est1 <- modsem(m1, oneInt)
summary(est1)By default, the model is estimated using the "dblcent" method. If you want to use another method, you can change it using the method argument.
est1 <- modsem(m1, oneInt, method = "lms")
summary(est1)modsem allows you to estimate interactions between not only latent variables but also observed variables. Below, we first run a regression with only observed variables, where there is an interaction between x1 and z2, and then run an equivalent model using modsem().
reg1 <- lm(y1 ~ x1*z1, oneInt)
summary(reg1)modsem
When you have interactions between observed variables, it is generally recommended to use method = "pind". Interaction effects with observed variables are not supported by the LMS and QML approaches. In some cases, you can define a latent variable with a single indicator to estimate the interaction effect between two observed variables in the LMS and QML approaches, but this is generally not recommended.
# Using "pind" as the method (see Chapter 3)
est2 <- modsem('y1 ~ x1 + z1 + x1:z1', data = oneInt, method = "pind")
summary(est2)modsem also allows you to estimate interaction effects between latent and observed variables. To do so, simply join a latent and an observed variable with a colon (e.g., 'latent:observer'). As with interactions between observed variables, it is generally recommended to use method = "pind" for estimating the effect between latent and observed variables.
m3 <- '
# Outer Model
X =~ x1 + x2 + x3
Y =~ y1 + y2 + y3
# Inner Model
Y ~ X + z1 + X:z1
'
est3 <- modsem(m3, oneInt, method = "pind")
summary(est3)Quadratic effects are essentially a special case of interaction effects. Thus, modsem can also be used to estimate quadratic effects.
m4 <- '
# Outer Model
X =~ x1 + x2 + x3
Y =~ y1 + y2 + y3
Z =~ z1 + z2 + z3
# Inner Model
Y ~ X + Z + Z:X + X:X
'
est4 <- modsem(m4, oneInt, method = "qml")
summary(est4)Here is a more complex example using the theory of planned behavior (TPB) model.
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 + INT:PBC
'
# The double-centering approach
est_tpb <- modsem(tpb, TPB)
# Using the LMS approach
est_tpb_lms <- modsem(tpb, TPB, method = "lms")
summary(est_tpb_lms)Here is an example that includes two quadratic effects and one interaction effect, using the jordan dataset. The dataset is 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
'
est_jordan <- modsem(m2, data = jordan)
est_jordan_qml <- modsem(m2, data = jordan, method = "qml")
summary(est_jordan_qml)Note: Other approaches also work but may be quite slow depending on the number of interaction effects, particularly for the LMS and constrained approaches.
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