Good morning, I'm running an ordered logistic regression
The model is the following
the dataset is
[CODE]
However when i try to obtain the estimated probability for the first regression this is the result
I was wonderng if coefficient obtained in odds are valid and if I can use them insead
And I was wondering if there is a solution to this problem
Thanks for your attention
The model is the following
Code:
eststo: quietly ologit satisfaction_overall i.migrant_groupw1 c.age##c.age i.education i.sex i.occupation i.Married i.year i.Countryoforign i.region_name if working==1, vce(cluster region_name)
[CODE]
Code:
* Example generated by -dataex-. For more info, type help dataex clear input int satisfaction_overall float migrant_groupw1 byte age float(education sex) byte occupation float Married int year float(Countryoforign region_name working) 9 0 34 6 1 2 1 2019 0 3 1 7 0 30 6 2 2 0 2015 0 3 1 10 0 60 2 1 8 1 2014 0 3 1 10 0 50 3 1 6 1 2014 0 3 1 5 0 50 3 2 6 1 2018 0 5 1 5 0 40 5 1 6 1 2017 0 1 1 10 0 39 5 2 3 1 2015 0 5 1 8 0 40 6 2 2 1 2017 0 19 1 8 0 60 3 1 6 1 2019 0 1 1 7 0 35 5 2 4 1 2014 0 9 1 7 0 29 6 1 4 0 2018 0 5 1 8 0 47 5 2 4 1 2016 0 9 1 8 0 55 5 2 3 1 2017 0 19 1 9 0 51 6 1 3 0 2019 0 3 1 10 0 64 2 2 6 1 2017 0 2 1 8 0 44 3 1 5 0 2018 0 5 1 8 0 59 6 2 3 1 2019 0 1 1 8 0 60 5 2 2 1 2014 0 17 1 6 0 34 5 1 6 1 2015 0 3 1 7 0 57 5 2 3 1 2015 0 19 1 7 0 42 3 2 6 1 2020 0 9 1 10 0 31 5 1 7 0 2018 0 5 1 7 0 61 6 2 2 1 2020 0 9 1 8 0 28 5 1 6 0 2020 0 1 1 6 0 47 3 1 6 0 2018 0 1 1 6 0 65 3 1 3 1 2019 0 15 1 6 0 52 3 1 5 1 2018 0 15 1 6 0 38 6 1 2 1 2017 0 6 1 9 0 32 3 1 6 1 2018 0 6 1 7 0 48 6 2 2 0 2018 0 19 1 8 0 48 5 2 3 1 2019 0 8 1 8 0 52 3 2 5 1 2015 0 12 1 8 0 33 6 1 2 0 2019 0 2 1 7 0 57 4 2 4 1 2018 0 6 1 9 0 40 6 2 2 0 2018 0 8 1 7 0 41 5 2 7 1 2016 0 1 1 8 0 75 6 1 2 1 2014 0 12 1 7 0 42 6 2 4 1 2020 0 19 1 10 0 22 5 1 3 0 2018 0 18 1 8 0 45 6 1 1 1 2017 0 5 1 8 0 18 3 1 8 0 2016 0 20 1 7 0 29 5 1 5 0 2017 0 10 1 6 0 47 6 2 3 0 2017 0 5 1 7 0 43 5 2 5 1 2020 0 20 1 7 0 45 4 1 7 0 2018 0 5 1 8 0 52 6 2 2 1 2016 0 5 1 7 0 23 5 2 4 0 2017 0 2 1 5 0 60 3 1 7 1 2018 0 8 1 6 0 51 3 1 8 1 2019 0 3 1 6 0 55 3 1 6 1 2015 0 13 1 8 0 60 3 2 8 1 2017 0 20 1 . 0 46 5 1 3 1 2016 0 3 1 7 0 57 4 2 4 1 2015 0 7 1 7 0 54 5 2 5 1 2020 0 1 1 8 0 64 5 1 4 1 2019 0 5 1 8 0 61 3 1 1 1 2020 0 10 1 7 0 48 3 1 6 1 2016 0 3 1 8 0 75 3 1 1 1 2019 0 3 1 8 0 44 4 2 3 1 2018 0 4 1 10 0 62 5 2 4 1 2020 0 5 1 9 0 38 5 1 4 0 2019 0 20 1 8 0 33 6 2 2 0 2014 0 1 1 . 0 35 5 1 6 1 2018 0 19 1 10 0 49 6 2 2 1 2014 0 5 1 8 0 48 3 1 7 0 2020 0 6 1 8 0 29 6 1 2 0 2020 0 9 1 9 0 55 3 1 6 0 2014 0 5 1 8 0 50 5 2 2 1 2017 0 8 1 7 0 39 6 2 2 0 2017 0 1 1 10 0 41 6 2 2 0 2015 0 20 1 9 0 49 3 1 6 1 2018 0 11 1 5 0 44 3 1 6 1 2016 0 16 1 10 0 46 3 1 6 1 2016 0 1 1 5 0 41 3 1 8 0 2014 0 19 1 7 0 48 5 2 3 1 2019 0 1 1 6 0 45 5 1 4 1 2014 0 5 1 8 0 32 5 1 3 0 2014 0 12 1 7 0 51 3 2 4 1 2016 0 3 1 7 0 57 3 2 5 1 2020 0 6 1 6 0 51 3 1 7 1 2014 0 9 1 7 0 49 5 2 5 1 2019 0 4 1 4 0 61 6 1 3 1 2019 0 7 1 6 0 45 6 2 4 1 2018 0 6 1 10 0 53 5 2 2 1 2020 0 12 1 5 0 55 3 2 6 1 2017 0 5 1 4 0 33 6 2 4 0 2019 0 18 1 7 0 53 5 1 5 1 2016 0 7 1 9 0 37 6 1 3 0 2020 0 1 1 8 0 23 5 2 5 0 2015 0 12 1 9 0 45 5 1 9 0 2019 0 6 1 6 0 51 3 1 7 1 2014 0 6 1 7 0 43 6 1 9 1 2019 0 3 1 8 0 43 5 2 5 1 2014 0 4 1 7 0 59 5 2 3 1 2017 0 4 1 8 0 31 4 1 5 0 2018 0 4 1 8 0 38 3 2 8 1 2015 0 20 1 5 0 44 3 1 7 1 2015 0 20 1 6 0 56 3 1 4 1 2015 0 9 1 6 0 51 3 2 8 1 2016 0 8 1 9 0 29 6 1 2 0 2017 0 3 1 8 2 32 3 1 5 1 2018 301 4 1 8 2 25 3 1 8 0 2018 301 4 1 7 2 26 3 1 5 0 2016 301 6 1 8 2 24 4 1 6 0 2017 301 8 1 8 2 25 5 1 8 1 2017 301 4 1 7 2 28 1 1 6 1 2020 301 17 1 7 2 28 3 1 8 0 2018 301 11 1 5 2 30 3 1 7 1 2014 301 8 1 7 2 25 3 1 8 1 2018 301 11 1 10 2 23 3 1 6 0 2017 301 8 1 7 2 32 3 1 6 1 2016 301 4 1 5 2 31 1 1 8 0 2019 301 12 1 8 2 28 3 1 7 0 2019 301 1 1 7 2 26 3 1 6 1 2018 301 11 1 7 2 34 3 1 8 1 2020 301 4 1 6 2 29 3 1 6 0 2017 301 8 1 5 2 24 3 1 3 1 2014 301 11 1 9 2 37 5 1 5 1 2019 305 12 1 6 2 40 5 1 6 1 2019 305 3 1 10 2 29 5 1 6 0 2014 333 4 1 8 2 31 3 1 8 1 2014 333 4 1 7 2 38 3 1 3 0 2020 333 4 1 7 2 26 5 1 8 0 2018 333 16 1 9 2 38 3 1 6 1 2016 333 4 1 5 2 22 5 2 8 1 2019 344 4 1 4 2 52 1 1 8 1 2019 344 5 1 9 2 20 3 1 8 0 2020 344 4 1 9 2 18 3 1 8 0 2020 344 4 1 7 2 21 1 1 8 0 2018 344 7 1 4 2 20 4 2 8 0 2018 344 4 1 0 2 45 5 1 8 0 2020 404 3 1 7 2 29 1 1 8 0 2019 404 20 1 5 2 20 3 2 5 0 2019 404 10 1 6 2 47 3 1 8 1 2019 404 15 1 8 2 19 4 1 6 0 2020 404 1 1 7 2 27 3 2 8 1 2020 404 8 1 6 2 25 3 2 8 1 2019 404 10 1 6 2 42 1 1 8 0 2020 404 9 1 7 2 57 5 1 8 1 2019 409 16 1 7 2 22 2 1 8 0 2019 409 20 1 7 2 21 3 1 8 0 2019 422 16 1 8 2 22 3 1 6 0 2020 422 19 1 5 2 21 3 1 8 0 2020 422 12 1 5 2 27 3 1 8 0 2020 422 16 1 7 2 21 1 1 6 0 2019 422 19 1 4 2 22 3 1 5 0 2020 422 19 1 6 2 31 2 1 8 1 2019 422 19 1 8 2 26 6 1 8 1 2019 422 8 1 0 2 28 5 1 8 0 2020 422 4 1 10 2 25 3 1 6 0 2020 422 4 1 7 2 22 3 1 6 0 2020 422 1 1 8 2 23 3 1 7 0 2018 423 8 1 8 2 30 3 1 5 0 2020 423 9 1 6 2 55 1 1 8 1 2020 423 15 1 6 2 36 2 1 8 0 2018 423 9 1 7 2 24 4 1 8 0 2020 423 5 1 7 2 32 2 1 6 0 2020 423 8 1 6 2 38 2 2 8 1 2018 423 19 1 10 2 32 5 1 6 1 2019 425 9 1 4 2 43 2 1 8 1 2019 425 15 1 9 2 21 3 1 6 1 2020 425 19 1 8 2 22 3 1 5 0 2019 425 2 1 3 2 20 3 1 8 0 2018 425 12 1 8 2 22 3 1 8 0 2018 425 12 1 3 2 21 1 1 8 0 2019 435 18 1 7 2 41 3 1 8 0 2019 435 11 1 4 2 26 3 1 8 1 2019 435 8 1 7 2 50 3 1 6 1 2014 435 18 1 8 2 32 2 1 6 1 2017 435 3 1 6 2 19 3 1 8 0 2020 435 1 1 5 2 21 1 1 8 0 2017 435 15 1 5 2 22 3 1 2 0 2019 435 19 1 8 2 26 3 1 6 0 2020 435 9 1 8 2 36 5 1 6 1 2018 435 3 1 6 2 39 2 1 8 1 2018 435 15 1 8 2 32 3 1 7 0 2018 435 5 1 6 2 33 1 1 6 1 2018 435 10 1 9 2 30 3 1 8 1 2020 435 4 1 7 2 30 3 1 5 0 2016 435 5 1 7 2 46 2 1 8 1 2019 435 12 1 7 2 29 5 1 8 0 2020 443 8 1 8 2 35 3 1 6 0 2020 443 1 1 6 2 29 2 1 8 0 2019 443 11 1 6 2 22 3 1 5 0 2018 443 12 1 8 2 26 3 2 5 0 2020 443 4 1 6 2 31 2 1 8 0 2018 443 16 1 10 2 28 5 1 5 0 2019 443 2 1 10 2 20 3 2 5 0 2018 443 12 1 5 2 31 1 1 8 0 2018 443 12 1 7 2 28 3 2 8 1 2020 443 5 1 6 2 22 3 1 8 0 2019 443 5 1 7 2 31 3 1 8 0 2019 443 17 1 5 2 37 6 1 6 1 2020 443 13 1 . 2 25 3 1 8 0 2019 443 10 1 8 2 25 3 1 6 1 2020 443 11 1 6 2 44 2 1 8 1 2020 443 6 1 7 2 26 5 1 8 0 2020 443 8 1 6 2 29 3 2 8 1 2020 443 15 1 6 2 24 3 1 8 0 2020 443 12 1 5 2 34 3 2 8 1 2020 443 5 1 5 1 20 3 1 6 0 2019 201 11 1 7 1 24 6 2 6 1 2019 201 6 1 4 1 23 3 1 4 0 2017 201 7 1 8 1 20 2 1 7 0 2014 201 3 1 5 1 68 5 1 7 1 2018 201 9 1 8 1 40 5 2 5 1 2014 201 3 1 6 1 28 3 2 5 0 2020 201 9 1 7 1 37 5 1 6 1 2020 201 7 1 7 1 27 3 2 8 1 2018 201 1 1 7 1 29 3 1 6 0 2015 201 8 1 7 1 30 3 1 6 1 2018 201 2 1 6 1 41 3 1 6 1 2015 201 1 1 8 1 33 3 1 6 1 2017 201 7 1 7 1 24 3 2 5 1 2015 201 17 1 2 1 35 3 2 5 1 2020 201 4 1 6 1 33 3 2 5 1 2016 201 6 1 7 1 31 3 1 6 1 2018 201 2 1 5 1 25 3 1 5 0 2019 201 19 1 8 1 30 6 1 5 1 2020 201 5 1 . 1 53 2 1 6 1 2015 201 3 1 7 1 24 5 1 7 0 2016 201 8 1 9 1 27 3 1 7 1 2019 201 1 1 7 1 32 3 1 6 1 2015 201 3 1 9 1 33 6 2 2 1 2017 201 1 1 7 1 42 3 1 6 1 2017 201 6 1 10 1 22 3 2 5 0 2018 201 10 1 10 1 24 3 1 5 1 2017 201 7 1 10 1 27 5 1 5 0 2017 201 9 1 4 1 25 6 2 8 1 2017 201 11 1 6 1 29 1 2 8 1 2017 201 10 1 5 1 52 3 1 8 1 2016 201 16 1 4 1 25 3 2 8 1 2019 201 9 1 9 1 60 4 1 5 1 2020 201 4 1 4 1 30 3 2 7 1 2020 201 9 1 8 1 24 5 1 8 0 2020 201 1 1 7 1 30 3 1 6 1 2020 201 16 1 8 1 31 4 1 6 0 2020 201 1 1 8 1 38 3 1 6 1 2015 201 3 1 7 1 36 3 2 5 1 2016 201 7 1 6 1 30 1 1 6 0 2017 201 7 1 7 1 35 3 2 8 1 2020 201 2 1 5 1 34 3 1 8 0 2019 201 1 1 8 1 31 5 1 6 1 2018 201 11 1 7 1 33 3 1 6 1 2019 201 11 1 7 1 27 6 2 8 1 2017 201 7 1 6 1 29 3 2 8 1 2020 201 8 1 9 1 31 3 1 6 1 2019 201 3 1 8 1 26 5 2 5 1 2018 201 10 1 4 1 22 3 1 8 0 2018 201 9 1 5 1 38 6 1 3 1 2016 201 1 1 8 1 30 3 1 6 1 2017 201 7 1 6 1 27 3 1 6 0 2018 201 8 1 9 1 30 5 1 8 1 2014 201 8 1 . 1 32 3 1 6 0 2017 201 8 1 7 1 48 5 1 5 0 2019 201 16 1 7 1 30 3 1 7 1 2018 201 11 1 8 1 58 3 1 8 1 2014 201 8 1 9 1 58 3 1 8 1 2018 201 1 1 7 1 27 3 2 7 1 2015 201 8 1 10 1 45 3 1 6 1 2019 201 4 1 8 1 31 3 2 8 1 2020 201 1 1 10 1 62 6 1 1 1 2019 201 6 1 7 1 25 3 2 5 1 2017 201 1 1 6 1 26 1 1 6 0 2014 201 7 1 8 1 24 4 1 7 0 2020 201 1 1 7 1 29 5 1 8 0 2019 201 8 1 8 1 35 3 1 8 1 2020 201 4 1 8 1 29 3 2 8 1 2018 201 7 1 7 1 38 3 1 8 1 2017 201 8 1 10 1 23 3 2 5 0 2019 201 2 1 6 1 25 5 2 8 1 2020 201 3 1 4 1 42 6 2 2 1 2019 201 8 1 7 1 26 6 2 8 1 2018 201 4 1 6 1 45 3 2 5 0 2017 201 14 1 6 1 34 4 1 8 0 2020 201 16 1 6 1 25 3 1 6 0 2016 201 18 1 9 1 25 3 2 7 1 2019 201 8 1 10 1 33 6 1 8 1 2018 201 7 1 . 1 38 6 2 5 1 2018 201 2 1 10 1 56 3 2 8 1 2019 201 6 1 8 1 35 3 2 5 1 2019 201 4 1 6 1 23 3 2 8 1 2019 201 16 1 5 1 23 5 2 8 1 2019 201 8 1 7 1 40 3 1 8 1 2016 201 8 1 3 1 46 3 1 8 1 2019 201 7 1 5 1 32 3 1 8 1 2016 201 10 1 8 1 35 5 1 6 1 2014 201 3 1 7 1 28 4 1 6 0 2017 201 1 1 7 1 39 4 1 6 1 2020 201 3 1 4 1 32 3 1 8 1 2019 201 8 1 9 1 31 6 2 5 1 2015 201 4 1 5 1 29 3 1 8 1 2019 201 19 1 6 1 27 1 2 8 1 2015 201 9 1 6 1 45 6 2 8 1 2018 201 11 1 9 1 40 3 1 5 1 2020 201 2 1 8 1 25 3 2 8 1 2015 201 4 1 10 1 41 3 2 5 1 2018 201 9 1 10 1 29 3 1 6 0 2014 201 9 1 10 1 28 3 1 6 0 2015 201 8 1 7 1 40 5 2 8 1 2016 201 2 1 end label values migrant_groupw1 vwi_all label def vwi_all 0 "Native", modify label values education w_all label def w_all 2 "Elementary education", modify label def w_all 3 "Middle school education", modify label def w_all 4 "Diploma 2-3 years", modify label def w_all 5 "Diploma 4-5 years", modify label def w_all 6 "Degree", modify label values sex x_all label def x_all 1 "Male", modify label def x_all 2 "Female", modify label values Countryoforign t_all label def t_all 0 "Italia", modify label values region_name l_all label def l_all 1 "Piemonte", modify label def l_all 2 "Valle d'Aosta", modify label def l_all 3 "Lombardia", modify label def l_all 4 "Trentino Alto Adige", modify label def l_all 5 "Veneto", modify label def l_all 6 "Friuli Venezia Giulia", modify label def l_all 7 "Liguria", modify label def l_all 8 "Emilia Romagna", modify label def l_all 9 "Toscana", modify label def l_all 10 "Umbria", modify label def l_all 11 "Marche", modify label def l_all 12 "Lazio", modify label def l_all 13 "Abruzzo", modify label def l_all 15 "Campania", modify label def l_all 16 "Puglia", modify label def l_all 17 "Basilicata", modify label def l_all 18 "Calabria", modify label def l_all 19 "Sicilia", modify label def l_all 20 "Sardegna", modify
Code:
. margins, dydx( migrant_groupw1)
Average marginal effects Number of obs = 224,567
Model VCE : Robust
dy/dx w.r.t. : 1.migrant_groupw1 2.migrant_groupw1
1._predict : Pr(satisfaction_overall==0), predict(pr outcome(0))
2._predict : Pr(satisfaction_overall==1), predict(pr outcome(1))
3._predict : Pr(satisfaction_overall==2), predict(pr outcome(2))
4._predict : Pr(satisfaction_overall==3), predict(pr outcome(3))
5._predict : Pr(satisfaction_overall==4), predict(pr outcome(4))
6._predict : Pr(satisfaction_overall==5), predict(pr outcome(5))
7._predict : Pr(satisfaction_overall==6), predict(pr outcome(6))
8._predict : Pr(satisfaction_overall==7), predict(pr outcome(7))
9._predict : Pr(satisfaction_overall==8), predict(pr outcome(8))
10._predict : Pr(satisfaction_overall==9), predict(pr outcome(9))
11._predict : Pr(satisfaction_overall==10), predict(pr outcome(10))
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| Delta-method
| dy/dx Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
0.migrant_groupw1 | (base outcome)
-------------------+----------------------------------------------------------------
1.migrant_groupw1 |
_predict |
1 | . (not estimable)
2 | . (not estimable)
3 | . (not estimable)
4 | . (not estimable)
5 | . (not estimable)
6 | . (not estimable)
7 | . (not estimable)
8 | . (not estimable)
9 | . (not estimable)
10 | . (not estimable)
11 | . (not estimable)
-------------------+----------------------------------------------------------------
2.migrant_groupw1 |
_predict |
1 | . (not estimable)
2 | . (not estimable)
3 | . (not estimable)
4 | . (not estimable)
5 | . (not estimable)
6 | . (not estimable)
7 | . (not estimable)
8 | . (not estimable)
9 | . (not estimable)
10 | . (not estimable)
11 | . (not estimable)
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Note: dy/dx for factor levels is the discrete change from the base level.
And I was wondering if there is a solution to this problem
Thanks for your attention
