Looking for help with including a moderator to a path model
blazej
Joined: 11/20/2018
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Greetings,
this is my 1st post, so please forgive me if this is offtopic.
In sort of new to SEM / modelling in general. So far I managed to make some sense of lavaan() syntax in R, to run a path model like this (file attached).
Before introducing the W moderator (continuous) this model was specified (in lavaan) as:
model <- '
X1 ~~ X2
A ~ X1 + X2
Y ~ A'
All variables are observed and continuous.
Now I'm trying to include the moderating effect of W on the effect of A on Y.
I have no clue on how to do it technically - syntax wise, and how to extract all of this from lavaan output for interpretation.
What I'm interested in is: whether or not W serves as a valid moderator and if yes how does it moderate the effect of A on Y.
Could anyone please give me a helpful hand?
I have openMX installed in my RStudio and don't mind switching to it - for now, as a beginner I found lavaan's syntax easier to understand.
new variable?
"_W_ moderates the path from _A_ to _Y_" is just another way of saying "_Y_ is being regressed onto an interaction between _A_ and _W_". I think the easiest thing to do would be to just make a new variable which is the product of _A_ and _W_--say, _AxW_--and make paths from _A_, _W_, and _AxW_ to _Y_.
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In reply to new variable? by AdminRobK
Thank you, that's what I was
Thank you, that's what I was looking for. All the best!
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In reply to Thank you, that's what I was by blazej
Another method
If W moderates the effect of A on Y, you could draw two pathways between A and Y: A -> Y and A->D->W, where D is a dummy latent variable with no variance other than that from A. Put the moderation parameter to be estimated on A->D and the definition variable W on A->D. The trick would be to label the path data.W (assuming that W is the moderator’s name in the dataset). The SE, CI or a likelihood ratio test against a model with the moderation parameter fixed to zero would inform about how unlikely the estimate obtained would be if the null hypothesis of no moderation was in fact true.
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