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| The '''`ologit`''' command fits a ordered logit model. | '''`-ologit-`''' fits an ordered logit model. |
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| In terms of syntax and reading output, `ologit` is very similar to [[Stata/Logit|logit]]. The most apparent difference is the inclusion of cuts. | When the dependent variable is categorical rather than binary, `-ologit-` should be used instead of [[Stata/Logit|-logit-]]. The two are otherwise very similar. |
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| {{{ . use https://www3.nd.edu/~rwilliam/statafiles/mroz.dta . gen lfstatus = cond(hours==0, 0, cond(inrange(hours,1,1249), 1, 2)) . label define lfstatus 0 "non-participation" 1 "part-time work" 2 "full-time work" . label values lfstatus lfstatus . ologit lfstatus kidslt6 kidsge6 age educ exper nwifeinc Iteration 0: log likelihood = -809.85106 Iteration 1: log likelihood = -686.68524 Iteration 2: log likelihood = -685.50088 Iteration 3: log likelihood = -685.49686 Iteration 4: log likelihood = -685.49686 Ordered logistic regression Number of obs = 753 LR chi2(6) = 248.71 Prob > chi2 = 0.0000 Log likelihood = -685.49686 Pseudo R2 = 0.1536 ------------------------------------------------------------------------------ lfstatus | Coefficient Std. err. z P>|z| [95% conf. interval] -------------+---------------------------------------------------------------- kidslt6 | -1.390614 .1813509 -7.67 0.000 -1.746055 -1.035172 kidsge6 | -.0341089 .0623172 -0.55 0.584 -.1562484 .0880307 age | -.0916151 .0121459 -7.54 0.000 -.1154207 -.0678096 educ | .158401 .0356408 4.44 0.000 .0885464 .2282557 exper | .1159833 .0112204 10.34 0.000 .0939917 .137975 nwifeinc | -.0153582 .0073408 -2.09 0.036 -.0297459 -.0009705 -------------+---------------------------------------------------------------- /cut1 | -1.75244 .7084357 -3.140949 -.3639319 /cut2 | -.3338748 .7054785 -1.716587 1.048838 ------------------------------------------------------------------------------ }}} The key is to recognize whether `ologit` or [[Stata/Mlogit|mlogit]] are more appropriate. Even when there is a natural ordering to the categories, `ologit` may not be a superior model. See As an example, adapted from [[https://www.statalist.org/forums/forum/general-stata-discussion/general/1653984-ordinal-or-multinomial-regression?p=1654012#post1654012]]: |
The key is to recognize whether `-ologit-` or [[Stata/Mlogit|-mlogit-]] is more appropriate. Even when there is a natural ordering to the categories, `-ologit-` may not be a superior model. See [[Stata/Mlogit#Usage|here]] for an example. |
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| [[https://www.stata.com/manuals/rologit.pdf|Stata manual for ologit]] | [[https://www.stata.com/manuals/rologit.pdf|Stata manual for -ologit-]] |
Stata ologit
-ologit- fits an ordered logit model.
Contents
Usage
When the dependent variable is categorical rather than binary, -ologit- should be used instead of -logit-. The two are otherwise very similar.
The key is to recognize whether -ologit- or -mlogit- is more appropriate. Even when there is a natural ordering to the categories, -ologit- may not be a superior model. See here for an example.
See also
Stata manual for ologit post-estimation