Hi!

I am new to Structural Equation Modeling but know that it is a confirmatory technique and one has to specify his\her own model and check if the data is supportive of the same. However, I have run into some illogical results using some very logical linkages in my model. In my model, one of the most logical linkages has a low negative regression weight which will be difficult to explain to the client. In such cases, would anyone recommend fixing the regression weight for this linkage to a low positive value (make the results face-valid)?

Thanks in advance!

The negative weight may be settable to zero without significant loss of fit. Try that and see.

You could also set the lbound to -.0001 and that will force the model to build a positive path if it wants to explain covariance on that path. Again: see what happens to fit.

Often there are equivalent mirror solutions: so everything is opposite in sign to what you are expecting. That just means that what you are thinking of as, say, "schooling" is being modelled as "lack of schooling" - the same scale reversed.

Because SEM generates net effects across the full set of paths between variables, you can get negative residuals when a cause has positive paths through other variables.

Two possibilities come to mind in that case: Either this cause has two factors buried inside it - the other paths are accounting for its positive effects leaving genuine negative effects left over, or else there is a better fitting model.

All of this is much easier to think about with a model, so try and include a drawing of your model.

Thanks! I tried setting the path co-efficient to 0 and it worked well.