totalme
totalme
Stata command for calculating holistic effect size measures for nominal and ordinal outcome models
Stata command for calculating holistic effect size measures for nominal and ordinal outcome models
totalme (Total Marginal Effect) is a companion package to Mize and Han's 2025 Sociological Science article "Inequality and total effect summary measures for nominal and ordinal variables."
totalme calculates total marginal effects (MEs) for independent variables in nominal and ordinal outcome models. For nominal independent variables, totalme calculates total ME inequalities.
To download the totalme command in Stata:
net install totalme, from("https://tdmize.github.io/data") replace
To read the help file (also available here):
help totalme
Please cite the use of the totalme command by citing the corresponding article:
Mize, Trenton D. and Bing Han. 2025. "Inequality and total effect summary measures for nominal and ordinal variables." Sociological Science. 12:115-157.
totalme can be used for a single model or to compare total MEs across two models.
For a single model, first fit the model and then call totalme with a list of independent variables for which total marginal effects should be calculated.
mlogit ...
totalme varlist
To compare across two models, first fit each individual model and store the estimates for each using est store. E.g.,
ologit dv iv1 iv2, vce(robust)
est store basemod
ologit dv iv1 iv2 med1 med2, vce(robust)
est store medmod
Note that totalme in the two model case uses the robust variance estimator. Thus, to match its results we strongly recommend you include vce(robust) as an option on the individual models.
Then, use totalme listing the independent variables you want total MEs calculated for and compared across models. Specify the two stored models( ) to compare.
totalme varlist, models(basemod medmod)
If the two models are fit on separate samples—such as in the analysis of groups—the group option is required.
totalme varlist, models(grp1mod grp2mod) group
This section shows how totalme can be used in a variety of analyses. These examples are replications of those in Mize and Han 2025.
This example uses a nominal model (a multinomial logit) with multiple independent variables.
use "https://tdmize.github.io/data/data/cda_gss", clear
drop if missing(healthR, race4, age, woman, parent, married, faminc, degree)
drop if year < 2000 | year > 2021
mlogit healthR i.race4 c.age i.woman i.parent i.married c.faminc i.degree
For continuous independent variables, custom amounts of change can be specified in the amount( ) option.
totalme age married parent, amount(sd)
Model (mod1) is:
mlogit healthR i.race4 c.age i.woman i.parent i.married c.faminc i.degree
Continuous/Binary IV(s):
age married parent
Total ME Estimates(N = 19292)
| Estimate Std. err. z P>|z|
-------------------------+-----------------------------------------------
age |
+ SD | 0.054 0.003 15.986 0.000
-------------------------+-----------------------------------------------
married |
Married vs Not Married | 0.031 0.007 4.737 0.000
-------------------------+-----------------------------------------------
parent |
Parent vs No Children | 0.027 0.009 2.976 0.003
All three independent variables have significant total effects. For example, the results for age suggest a standard deviation increase in age affects self-rated health by about 5.4 percentage points in total.
This example fits two models on the same sample and compares the total ME estimate across models, as with tests of mediation/attenuation.
use "https://tdmize.github.io/data/data/cda_gss", clear
drop if missing(healthR, college, race4, age, woman, parent, married, faminc)
drop if year < 2000 | year > 2021
Each model should first be fit and the estimates stored.
mlogit healthR i.college i.race4 c.age i.woman i.parent i.married, vce(robust)
est store basemod
mlogit healthR i.college i.race4 c.age i.woman i.parent i.married c.faminc, vce(robust)
est store medmod
With two models, specify the name of the stored models in the models( ) option of totalme.
totalme college, models(basemod medmod)
Model 1 (basemod) is:
mlogit healthR i.college i.race4 c.age i.woman i.parent i.married, vce(robust)
Model 2 (medmod) is:
mlogit healthR i.college i.race4 c.age i.woman i.parent i.married c.faminc, vce(robust)
Continuous/Binary IV(s):
college
Total ME Estimates(N_basemod = 19292 , N_medmod = 19292)
| Estimate Std. err. z P>|z|
-------------------------+-----------------------------------------------
college |
Model 1 (basemod) | 0.159 0.006 26.338 0.000
Model 2 (medmod) | 0.118 0.007 17.514 0.000
Cross-Model Diff. | 0.041 0.003 15.006 0.000
The total ME for college is significantly attenuated after accounting for the mediator, family income.
This example combines nominal/ordinal outcomes with ordinal/nominal independent variables. In this case, totalme calculates a total ME inequality for each ordinal/nominal independent variable.
use "https://tdmize.github.io/data/data/cda_gss", clear
drop if missing(healthR, race4, age, woman, parent, married, faminc, degree)
drop if year < 2000 | year > 2021
mlogit healthR i.race4 c.age i.woman i.parent i.married c.faminc i.degree
totalme race4 degree
Model (mod1) is:
mlogit healthR i.race4 c.age i.woman i.parent i.married c.faminc i.degree
Nominal IV(s):
race4 degree
Total ME Estimates(N = 19292)
| Estimate Std. err. z P>|z|
-------------------------+-----------------------------------------------
race4 |
total ME Ineq. | 0.047 0.007 6.937 0.000
-------------------------+-----------------------------------------------
degree |
total ME Ineq. | 0.116 0.006 20.894 0.000
The results suggest that racial-ethnic groups differ by a total of 4.7 percentage points in terms of their self-rated health ratings, and those with different educational degrees differ by a total 11.6 percentage points.
The total ME inequality reported above from totalme is a single summary measure across all outcome categories. To obtain individual ME inequality estimates for each outcome category, use the meinequality command.
meinequality race4 degree
Model (mod1) is:
mlogit healthR i.race4 c.age i.woman i.parent i.married c.faminc i.degree
ME Inequality Estimates (N = 19292)
| Estimate Std. err. z P>|z|
-------------------------+-----------------------------------------------
race4 ME Ineq. |
Pr(Poor) | 0.012 0.004 2.926 0.003
Pr(Fair) | 0.033 0.006 5.369 0.000
Pr(Good) | 0.010 0.008 1.225 0.221
Pr(Excellent) | 0.039 0.006 6.112 0.000
-------------------------+-----------------------------------------------
degree ME Ineq. |
Pr(Poor) | 0.034 0.003 12.102 0.000
Pr(Fair) | 0.079 0.005 15.755 0.000
Pr(Good) | 0.034 0.006 5.744 0.000
Pr(Excellent) | 0.085 0.006 15.223 0.000