As the x-variables become more strongly correlated, it becomes more difficult to determine which of the variables are actually producing the statistical effect on the y-variable. This is the problem with multicollinearity.  

One way of assessing multicollinearity is using the estat vce command, with the corr (short for correlation) option.  

More information help estat vce

Practical example

The first step is re-run the multiple ordinal regression model. The quietly option is included in the beginning of the command to suppress the output. 

quietly ologit educ gpa bullied ib1.bestfriends if pop_ordinal==1

Next, we try the estat vce command. By adding the corr (=correlation) option, we will get a correlation matrix instead of a covariance matrix. 

estat vce, corr

The table shows the correlations between the different variables/categories. In line with the earlier sections on correlation analysis (see Correlation analysis), we can conclude that the coefficients suggest weak to moderate correlations here (with the exception of the dummies of bestfriends, which is not a problem since they reflect the same underlying variable).