Path Analysis

Dear Sarah,

As you know, our discussion continued off-list, and I'm glad that you were
able to get the software to work.

I'll address your question briefly, but what I have to say probably isn't
what you want to hear:

Most fundamentally, the information you've provided is entirely without
content. That is, variable names like x1 and y1 convey no information about
the substance of the data. It's therefore impossible to know whether the
model that you specified is sensible. I think that you'd do much better to
seek competent statistical help locally than to ask questions on an email
list devoted to statistical software.

That said, you've specified a very restrictive model for the data. You could
add 8 paths to the model and still have a fully recursive model. For
example, your model specifies that x2 can only influence y4 indirectly
through y1. If you've carefully specified the model and believe, for
example, that the missing paths are implausible, that x1 and x2 are really
exogenous, and that all of the disturbances are uncorrelated, then the
correct conclusion is that your model is wrong. You could try adding the
missing paths to the model, but if you're willing to do this that would
suggest that you didn't think carefully enough about the specification in
the first place. In my opinion, structural-equation modeling shouldn't be
regarded as an exploratory method.

Of course, in a very large sample, an overidentified model that's trivially
wrong can be rejected when tested as a hypothesis. I don't know how large
your sample is, but the various "fit indices" are not encouraging. Your
model isn't just trivially wrong. Moreover, the R^2s for the endogenous
variable are very small -- two are effectively 0.

I can't judge whether your model makes any sense, but it's my impression
that most structural equation models don't. People often think that SEMs are
magic wands that can be waved over observational data to draw causal
inferences, even when the assumptions underlying the model, such as
exogeneity, are implausible, and without attending to aspects of the model,
such as potential nonlinearity, that should be part of careful regression
modeling.

My two cents,
 John

Path Analysis

Thread (4 messages)