Context: If your dependent variable is ordered, like 'low', 'middle', 'high' levels of income, you may want to use ordinal logistic regression. An important assumption of this technique is that the set of coefficients estimated for each level of income needs to be similar with other two sets. We need to check for that. If this assumption is not violated, we report the coefficients by ordinal logistic regression. If it is violated, we should use multinominal logistic regression. This type of regression does not require stability of the sets of coefficients.
How to do in STATA?
It is the syntax in stata,
ologit y x 1 x2...................... xn
brant, detail
Significant results mean this assumption is violated. Large sample size is sensitive to this test. Recode the violating variable and re-run.
If brant still does not work, download omodel, and run this: (from here)
omodel y x1 x2 x2
Significant results mean that assumption is violated! In other words, coefficients do vary significantly between categories of the response variabl.
Sample size is also a problem. More samples are needed in ologit model than OLS model.
If both tips do not work, go for gologit2, see here
How to do in STATA?
It is the syntax in stata,
ologit y x 1 x2...................... xn
brant, detail
Significant results mean this assumption is violated. Large sample size is sensitive to this test. Recode the violating variable and re-run.
If brant still does not work, download omodel, and run this: (from here)
omodel y x1 x2 x2
Significant results mean that assumption is violated! In other words, coefficients do vary significantly between categories of the response variabl.
Sample size is also a problem. More samples are needed in ologit model than OLS model.
If both tips do not work, go for gologit2, see here
