Dear all,
I am trying to examine the relationship between education and a woman’s probability of getting married, using a discrete time logistic regression model. The dependent variable is married (=1 or 0). For controls, I have a categorical variable for the individual’s own level of education, edu_cat (where 0 is no education, 1 and 2 are primary and secondary schooling respectively), and a bunch of other predictors. My Stata code and results are below.
I am presenting my analysis at a meeting, and would like to make my results more visually engaging, thereby presenting graphs rather than a bunch of regression tables. I was just wondering what command to use if I want to graph the relationship between the outcome variable, married, (which is binary) and the dependent variable, edu_cat, which is categorical.
Thank you!
Monzur
I am trying to examine the relationship between education and a woman’s probability of getting married, using a discrete time logistic regression model. The dependent variable is married (=1 or 0). For controls, I have a categorical variable for the individual’s own level of education, edu_cat (where 0 is no education, 1 and 2 are primary and secondary schooling respectively), and a bunch of other predictors. My Stata code and results are below.
Code:
. logistic married pyear pyearsq i.edu_cat i.bict muslim father_5andaboveeduc mother_5andaboveeduc hh_lowinc i.rural_div [pw= popweight] Logistic regression Number of obs = 30,336 Wald chi2(16) = 3150.08 Prob > chi2 = 0.0000 Log pseudolikelihood = -1.856e+08 Pseudo R2 = 0.1996 -------------------------------------------------------------------------------------- | Robust married | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ---------------------+---------------------------------------------------------------- pyear | 5.214708 .3749294 22.97 0.000 4.529288 6.003853 pyearsq | .9621039 .0020803 -17.87 0.000 .9580353 .9661898 | edu_cat | 1 | .9944425 .0526095 -0.11 0.916 .8964955 1.103091 2 | .7840981 .0457952 -4.16 0.000 .699288 .8791941 | bict | birthcohort 1976-86 | 1.072139 .0569778 1.31 0.190 .9660838 1.189837 birthcohort 1987-97 | 1.036028 .0625042 0.59 0.557 .9204877 1.166071 | muslim | 1.299013 .0886869 3.83 0.000 1.136318 1.485003 father_5andaboveeduc | .8964348 .0458894 -2.14 0.033 .8108581 .9910432 mother_5andaboveeduc | .8650096 .0561416 -2.23 0.025 .7616852 .9823502 hh_lowinc | 1.054659 .0639445 0.88 0.380 .9364904 1.187738 | rural_div | Barisal | 1.185689 .1008533 2.00 0.045 1.003619 1.40079 Chittagong | 1.04448 .0626355 0.73 0.468 .9286565 1.174749 Khulna | 1.447291 .1105905 4.84 0.000 1.245988 1.681117 Rajshahi | 1.298267 .090639 3.74 0.000 1.132236 1.488644 Rangpur | 1.429192 .1046735 4.88 0.000 1.238081 1.649804 Sylhet | .7301493 .0478659 -4.80 0.000 .6421108 .8302585 | _cons | 1.47e-08 8.71e-09 -30.37 0.000 4.58e-09 4.70e-08 --------------------------------------------------------------------------------------
Thank you!
Monzur
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