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From |
"Michael N. Mitchell" <Michael.Norman.Mitchell@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: margins after xtlogit |

Date |
Fri, 10 Sep 2010 22:41:51 -0700 |

Dear Traci

margins race1, predict(pu0)

-help logit postestimation- under the section on predict that will say "pr probability of a positive outcome; the default"

I hope that helps, Michael N. Mitchell Data Management Using Stata - http://www.stata.com/bookstore/dmus.html A Visual Guide to Stata Graphics - http://www.stata.com/bookstore/vgsg.html Stata tidbit of the week - http://www.MichaelNormanMitchell.com On 2010-09-10 4.59 PM, Traci Schlesinger wrote:

hi all: i am analyzing racial disparities in pretrial diversions (a yes no, i.e. 0/1, criminal justice outcome) using individual level data from the SCPS, which is clustered by county--an observation for every individual charged with a felony in sampled counties is included. to account for the county level sampling, i'm using xtlogit with county level random effects. however, i'm having difficulty interpreting the results from margins after xtlogit. if i estimate a model with logistic and then ask for margins on race i get: . margins race1, post Predictive margins Number of obs = 46019 Model VCE : OIM Expression : Pr(diversion), predict() ------------------------------------------------------------------------------ | Delta-method | Margin Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- race1 | 1 | .1025184 .0023145 44.29 0.000 .097982 .1070548 2 | .0848741 .0020596 41.21 0.000 .0808374 .0889109 3 | .0858849 .0023203 37.01 0.000 .0813372 .0904327 ------------------------------------------------------------------------------ which i interpret as meaning that if everyone in my sample were white (race1 = 1), 10% of defendants would be offered pretrial diversions. if everyone were black (race1=2), only 8% of defendants would be offered pretrial diversions. (race1=3 are Latinos, with 8.5% of people getting diversions). however, if i estimate xtlogit --either getting my results as coefficients or odds-ratios-- and then margins, i get the following table. . margins race1, post Predictive margins Number of obs = 46019 Model VCE : OIM Expression : Linear prediction, predict() ------------------------------------------------------------------------------ | Delta-method | Margin Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- race1 | 1 | -3.580741 .2277247 -15.72 0.000 -4.027073 -3.134409 2 | -3.919428 .2274633 -17.23 0.000 -4.365248 -3.473608 3 | -3.67982 .2301685 -15.99 0.000 -4.130942 -3.228698 ------------------------------------------------------------------------------ i am at a loss as to how to interpret this. for starters, it seems strange that all three racial groups have negative margins. also, i'm clearly not looking at the percent of defendants who get a pretiral diversion any more. i've looked through the manual, but have not been able to figure this out. i would appreciate any help. cheers, traci * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

* * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**Follow-Ups**:**Re: st: margins after xtlogit***From:*Steve Samuels <sjsamuels@gmail.com>

**Re: st: margins after xtlogit***From:*Traci Schlesinger <traci.schlesinger@gmail.com>

**References**:**st: margins after xtlogit***From:*Traci Schlesinger <traci.schlesinger@gmail.com>

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