These latent variables are based on observed ordinal data. Econ is latent economic perceptions, Cmp_g is latent government competence. Cmp_o is latent opposition party competence. Support is latent government support. Coefficients are standardised.
My problem is that I'm not sure how to interpret the coefficients in the structural model. Ordinarily, I would have treated the variables as continuous, but they are highly skewed and I'm trying to get out of bad habits. So, do the regression coefficients on the paths (e.g. .713 from Econ to Cmp_g) represent change in the assumed latent normal distribution? Or are they still in probits?
Any help would be greatly appreciated.
So, do the regression coefficients on the paths (e.g. .713 from Econ to Cmp_g) represent change in the assumed latent normal distribution?
Or are they still in probits?
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I interpreted the question differently from how Terrence did. Wouldn't it be the case that the latent variables themselves are continuous variables, it is the relationship from the latents to the indicators where the ordinal nature of the items comes into play? As such, the associations between the latents are interepretable just as they would be were the indicators continuously scaled?
On Tue, Jul 17, 2018 at 7:24 AM, Terrence Jorgensen <tjorge...@gmail.com> wrote:
So, do the regression coefficients on the paths (e.g. .713 from Econ to Cmp_g) represent change in the assumed latent normal distribution?
YesOr are they still in probits?
That's what a probit is. The probit function links the latent response to the observed response. So any estimated effect on an ordered outcome is interpreted in probit units, not as changes in actual probability.Terrence D. JorgensenPostdoctoral Researcher, Methods and StatisticsResearch Institute for Child Development and Education, the University of AmsterdamUvA web page: http://www.uva.nl/profile/t.d.jorgensen
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