Solved – Moderated mediation with latent variables

I am trying to make a moderated mediation, where the moderator (W) is categorical and Y and X are latent variables. However, none of the approaches I found either here or in Pubmed.

Please consider the model from the paper linked:

enter image description here

Whose specification looks like:

> model7 <- “ +  del ~ cprime * male +  del ~ b * respect +  del ~ bprime * maleXResp +  respect ~ a * male +  respect ~~ maleXResp +  male ~~ maleXResp + +  bmale:= b + bprime +  indMale:= bmale * a +  indFemale:= b * a +  indDiff:= indMale – indFemale +  ” > > fit7 <- sem(model7, data=d, fixed.x=FALSE, +      se=“bootstrap”) > summary(fit7) 

Full citation for this paper is:

Jeremy N.V Miles, Magdalena Kulesza, Brett Ewing, Regina A Shih, Joan S Tucker, Elizabeth J D'Amico, (2015) "Moderated mediation analysis: an illustration using the association of gender with delinquency and mental health", Journal of Criminal Psychology, Vol. 5 Issue: 2, pp.99-123, 

In the data I have access to, both Deliquency and Respect are latent variables.

  • How would I specify the mediation in the case where deliquency and respect are latent variables?

In my attempt, lavaan complains of Respect not being an observed variable, I can't figure out how to approach this issue.

I found the answer and it is quite simple, but introduces new assumptions to the analysis.

One can create a multiplicative term using indProd() from semTools() like this:

dataset<-indProd(dataset, var1=c("item1","item2","item3"),                       var2=c("second1","second2","second3")) 

And then create a latent variable that regresses on this term:

model7 <- '     ...     interaction =~ item1.second1 + item2.second2 + item3.second3     ... ' 

The remainder of the model is similar to the one in the question, using a mediation approach but including interaction.

Similar Posts:

Rate this post

Leave a Comment