Ordered probit vs OLS

[Vishna Senathirajah]

Hi all,
I was unsure on how to compare the results from an ordered probit model and an OLS regression. I wanted to use an OLS for ease of interpretation in a paper however an ordered probit makes more sense to use with my DV as it is categorical. I wanted to try and see if they produce similar results and any guidance on how to do so would be much appreciated.
Thanks in advance,
Vishna

[Carlo Lazzaro]

Vishna:
welcome to this forum.
Using the wrong estimator just because its results are easier to get/disseminate than those provided by the right estimator is not advisable.
If your regressand is ordered, -OLS- cannot read that (and your coefficients will be unreliable).
In the following toy-example I assume that -rep78- is ordinal:

. use "C:\Program Files\Stata17\ado\base\a\auto.dta"
(1978 automobile data)

. ologit rep78 mpg i.foreign

Iteration 0:   log likelihood = -93.692061  
Iteration 1:   log likelihood = -78.844995  
Iteration 2:   log likelihood = -78.095292  
Iteration 3:   log likelihood = -78.089244  
Iteration 4:   log likelihood = -78.089242  

Ordered logistic regression                             Number of obs =     69
                                                        LR chi2(2)    =  31.21
                                                        Prob > chi2   = 0.0000
Log likelihood = -78.089242                             Pseudo R2     = 0.1665

------------------------------------------------------------------------------
       rep78 | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         mpg |   .0672774   .0494465     1.36   0.174     -.029636    .1641908
             |
     foreign |
    Foreign  |   2.599085   .6745635     3.85   0.000     1.276964    3.921205
-------------+----------------------------------------------------------------
       /cut1 |  -1.885212   1.175719                      -4.18958    .4191557
       /cut2 |  -.0922329   .9934141                     -2.039289    1.854823
       /cut3 |   2.524538   1.021289                      .5228484    4.526228
       /cut4 |   4.580877   1.146848                      2.333095    6.828658
------------------------------------------------------------------------------

. reg rep78 mpg i.foreign

      Source |       SS           df       MS      Number of obs   =        69
-------------+----------------------------------   F(2, 66)        =     19.65
       Model |  24.8699108         2  12.4349554   Prob > F        =    0.0000
    Residual |  41.7677703        66  .632845005   R-squared       =    0.3732
-------------+----------------------------------   Adj R-squared   =    0.3542
       Total |  66.6376812        68  .979965899   Root MSE        =    .79552

------------------------------------------------------------------------------
       rep78 | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         mpg |   .0283844   .0184545     1.54   0.129    -.0084611      .06523
             |
     foreign |
    Foreign  |   1.101839   .2335733     4.72   0.000     .6354952    1.568183
       _cons |   2.466154   .3784698     6.52   0.000     1.710515    3.221793
------------------------------------------------------------------------------

.

[Vishna Senathirajah]

Thanks, Carlo, I'll use the ordered probit instead then.
Kind Regards,
Vishna