1. Using Stata’s Margins Command
Predicted Values, Predicted Probabilities, and Graphs
A Very Practical (Not Theoretical) Guide

2. Outline Factor Variables Margins Command Possibilities
Predicted Values
Predicted Probabilities
Graphs

3. Factor Variables--What Are They and Why Do They Matter?
Factor variables are Stata-specific notation for identifying categorical and continuous INDEPENDENT variables
Instead of typing…
reg depvar indepvar1 indepvar2
Type…
reg depvar i.indepvar1 c.indepvar2
This tells Stata that indepvar1 is a categorical variable and to treat it as such
And that indepvar2 is a continuous variable
This means you don’t have to create variables anymore!
No need to create dummies for race, marital status, religious affiliation, etc.
As we’ll see momentarily, it also means you don’t need to create interaction variables either. Stata will do both of those things for you!

4. Factor Variables—Menu

5. Factor Variables—Syntax
Stata command indicating a linear regression
reg MaritalInt c.age i.race c.educ c.sibs i.cohab i.prevmar
Dependent Variable
Independent Variables (’c.’ indicates a continuous variable while ’i.’ indicates a categorical one.

6. The Margins Command
Designed to provide predicted values of Y for a particular combination of your x’s, along with many other things.
Say you have the following regression-->
You’re trying to predict marital interaction. But how do I make sense of the race differences?

7. The Margins Command The margins command to the rescue!
margins race, atmeans vsquish
That is, the model predicts that Asians have the highest level of marital interaction, followed by Hispanics and Whites.

8. The Margins Command Now you can graph this
margins, noci
Or make it fancier (and a bar plot)
marginsplot, noci recast(bar) title("Adjusted Predictions of Marital Interaction, by Race (CREATE Wave 1)") ///ytitle(Predicted Marital Interaction) xtitle("") text( "This bar is", place(e) size(small)) text( "too low", place(e) size(small))

9. Graphing Interaction Terms
reg MaritalInt age ib(3).race educ sibs i.cohab i.prevmar c.age#i.prevmar c.age#c.educ

10. Graphing Interaction Terms

11. Graphing Interaction Terms
This is the categorical variable
This is the continuous variable. Specifiy the values you want to get the predicted values in the box below

12. Graphs

13. Now do it with syntax Regression
reg MaritalInt c.age ib(3).race c.educ c.sibs i.cohab i.prevmar c.age#i.prevmar c.age#c.educ i.prevmar#i.cohab
Continuous X categorical interaction
margins prevmar, at(age=(20(10)60)) atmeans vsquish
maginsplot, noci
marginsplot, noci ytitle(Predicted Marital Interaction) xtitle(Age) ytitle(Pred. Marital Interaction) title(Age->Marital Interaction Depends on If Previously Married)
Continuous X continuous interaction
margins, at(age=(20(10)60) educ=(0/3)) atmeans vsquish
marginsplot, noci
marginsplot, noci ytitle(Predicted Marital Interaction) xtitle(Age) ytitle(Pred. Marital Interaction) title(Age->Marital Interaction Does Not Depend on Education)
Categorical X Categorical
margins cohab, at(prevmar=(0/1)) atmeans vsquish
marginsplot, noci ytitle(Predicted Marital Interaction) xtitle(Previously Married) title(PrevMar->Marital Interaction Depends on PreMar Cohabitation)
Simple Slopes
margins, dydx(cohab) at(prevmar=(0/1)) vsquish

14. Predicted Probabilities
Same process, virtually identical syntax, just trade out `reg’ for `logit’. For menus, it’s much the same except you select ‘Statistics->Binary Outcomes->Logistic regression, reporting coefficients’. I’ll forgo showing the menus for now.
Syntax
logit MaritalDummy age ib(3).race educ sibs i.cohab i.prevmar
margins race, atmeans vsquish
marginsplot, noci recast(bar)
Note: MaritalDummy = 0 if below median on marital interaction, 1 if above median on

15. Predicted Probabilities
What do these numbers mean?

16. Predicted Probabilities
Translate the log-odds into predicted probabilites (the probability that Other Race, Black, Asian, Hispanic, or White = 1) with the margins command
margins race, atmeans vsquish

17. Predicted Probabilities
Graph Those Probabilities!
marginsplot, noci recast(bar)

18. Predicted Probabilities—Interaction Term
logit MaritalDummy age ib(3).race educ sibs i.cohab i.prevmar c.age#i.prevmar

19. Predicted Probabilities—Interaction Term
margins prevmar, at(age=(20(10)60)) atmeans vsquish

20. Predicted Probabilities—Interaction Term
marginsplot, noci xtitle(Age) ytitle(Predicted Prob. of High Marital Interaction) title(Age->Marital Interaction Depends on If Previously Married)

21. Conclusion
The margins command is a compelling tool for helping us visualize and understand our data, all while saving us time
The use of factor variables eliminates the need to create interaction or dummy variables
We can quickly and easily translate our results into meaningful units, ensuring maximum comprehension of our data
The marginsplot command makes it simple and quick to create graphs