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I have recently started to improve my methodological skills and programming in R. For a study term paper I now want to run an ordered logit/probit model. The data I use are taken from the European Social Survey, which I have merged over all available years, and built subsets containing the variables of interest, that are:

Dependent variable -> “happiness” (ranging from 0 to 10, with 0 = “Extremely unhappy” and 10 = “Extremely happy”) Independent variables -> “Income” (ranging from 1 to 10), “health” (ranging from 1 to 5), “religion” (ranging from 0 to 10) and “personal relationships” (ranging from 1 to 7)

Of main interest is the relationship between “happiness” and “income”, however, I want to control for further factors.

My research so far suggests that an ordered logit/probit regression model is the most appropriate here. I have read some literature regarding this method, and am not sure about some points.

  • As far as I understand, a latent variable that is not observable influences the answering process and thereby the model and its results. Do I need to program this latent variable manually, or is it “automatically” done by R (I have used the MASS package so far, but oglmx was further suggested to me, to control for heterogeneiety)?
  • I am not sure about the “threshold parameters”. Again, do I need to program them as well, or is it done automatically? And what exactly are they good for?
  • Furthermore, I am interested in, how exactly to interpret the outcome. Is it possible to interpret the coefficients at all, or just the fore sign?

Additionally to my questions, I am very open to all sorts of tips regarding this model (or more appropriate ones?), due to the fact that I am new to this and want to learn.

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  • $\begingroup$ A couple points for clarification to assist in answering: ¿how many variables are being used for your "latent" constructs (i.e., the dependent variable is happiness, and it is measured by only one ordinal variable...or are there others)? ¿Are you familiar with the lavaan package? $\endgroup$ – Gregg H Apr 19 '18 at 16:21

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