I have three questions about SEM and I'll ask the questions using an example that I have been working with. The dataset has employment (
emp), addiction (
addict), depression (
dep) and suicidal thoughts (
suicide)-- all of which are binary (yes/no). I want to test all bidirectional relationships among
dep and also their association to
suicide. You can see the following syntaxes to understand my hypothesized model.
simple_bider <- ' # paths emp ~ addict + dep addict ~ emp + dep dep ~ emp + addict suicide ~ dep + addict + emp ' fit <- sem(simple_bider, data=dat) summary(fit, standardized=TRUE)
At first I made these variables ordered to get the diagonally weighted least squares estimates as instructed in the lavaan tutorial page. However, as I'm testing the bidirectional relationships I have to put the same variable both as a predictor and then as an outcome within the model statements. Is there a problem with that when I'm making them ordered?
How does this model differ from doing separate probit regressions and by doing an SEM do we actually achieve anything over just confirming associations?
Unfortunately, this syntax produces the following errors/warnings:
> fit <- sem(simple_bider, data=dat) Warning message: In lav_model_vcov(lavmodel = lavmodel, lavsamplestats = lavsamplestats, : lavaan WARNING: could not compute standard errors! lavaan NOTE: this may be a symptom that the model is not identified.
What I read from different sources is, for a model to be not identified there has to be fewer "knowns" than "unknowns". So, what are the "knowns" and "unknowns" here and how can I tell why the model is not identified here? I suppose adding more observations will NOT help the model be identified, is that correct? Would adding some covariates make the number of "unknowns" higher and cause more problems?