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Highlights

• Value labels on factor variables now appear in estimation output
• Value labels appear on contrasts, margins, and pairwise comparisons

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A factor variable might be

• attitude measured on a scale of 1 to 5,
• agegrp recorded 1 to 4, 1 being 20-30, 2 being 31-40, ...
• region being 1 (North East), 2 (North Central), ...

When you fit a model, Stata allows factor-variable notation. You can type

i.attitude

to obtain the levels of factor variable attitude.

i.attitude#c.age

to obtain the levels of attitude interacted with continuous variable age

i.attitude##c.age

to mean i.attitude age i.attitude#c.age

i.attitude#i.agegrp

to obtain the levels of attitude interacted with the levels of agegrp

i.attitude##i.agegrp

to mean i.attitude i.agegrp i.attitude#i.agegrp

i.attitude#i.agegrp#i.region

to obtain the levels of attitude interacted with the levels of agegrp interacted with the levels of region

i.attitude##i.agegrp##i.region

to mean

	i.attitude  i.agegrp  i.region 
	i.attitude#i.agegrp   i.attitude#i.region 
	i.agegrp#i.region     
	i.attitude#i.agegrp#i.region 

i.(attitude agegrp)

to mean i.attitude i.agegrp

i.(attitude agegrp)##i.region

to mean i.attitude##i.region i.agegrp##i.region

and so on.

Stata also has value labels. You might type

. label define regions 1 "North East"  2 "North Central"
                       3 "South"       4 "West"

. label values region regions

In Stata 13, when you fit a model using factor-variable notation, the labels appear in the output:

. regress  y  i.attitude i.agegrp i.region


      Source 
 
       SS       df       MS   
           Number of obs =     400

 
 
 
           F( 10,   389) =   22.60

       Model 
 
  2668.04079    10  266.804079
           Prob > F      =  0.0000

    Residual 
 
  4592.44366   389  11.8057678
           R-squared     =  0.3675

 
 
 
           Adj R-squared =  0.3512

       Total 
 
  7260.48445   399  18.1967029
           Root MSE      =   3.436





              y 
 
      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]

 
 
 

       attitude 
 
 

      disagree  
 
    1.27901   .5617435     2.28   0.023     .1745764    2.383443

       neutral  
 
   1.466543   .5304032     2.76   0.006     .4237268    2.509358

         agree  
 
   2.063136   .5326997     3.87   0.000     1.015805    3.110467

strongly agree  
 
   3.550927   .5801312     6.12   0.000     2.410343    4.691512

                
 
 

         agegrp 
 
 

         31-40  
 
   2.114168   .4868806     4.34   0.000     1.156921    3.071414

         41-50  
 
   3.970627   .4866537     8.16   0.000     3.013826    4.927428

           50+  
 
   5.990408   .4869362    12.30   0.000     5.033052    6.947764

                
 
 

         region 
 
 

 North Central  
 
    .673176   .4913976     1.37   0.172    -.2929515    1.639304

         South  
 
  -1.366099    .491862    -2.78   0.006     -2.33314   -.3990588

          West  
 
  -1.477714   .4890703    -3.02   0.003    -2.439266   -.5161623

                
 
 

          _cons 
 
   8.411983   .5760115    14.60   0.000     7.279498    9.544468

Value labels are also used by Stata's postestimation commands. Below we use pwcompare to compare y values for each pairing of the age groups:

. pwcompare agegrp

Pairwise comparisons of marginal linear predictions

Margins      : asbalanced




                
 
                                 Unadjusted

                
 
   Contrast   Std. Err.     [95% Conf. Interval]

 
 
 

         agegrp 
 
 

31-40 vs 20-30  
 
   2.114168   .4868806      1.156921    3.071414

41-50 vs 20-30  
 
   3.970627   .4866537      3.013826    4.927428

  50+ vs 20-30  
 
   5.990408   .4869362      5.033052    6.947764

41-50 vs 31-40  
 
   1.856459   .4869484      .8990793    2.813839

  50+ vs 31-40  
 
    3.87624   .4870898      2.918582    4.833898

  50+ vs 41-50  
 
   2.019781   .4878207      1.060686    2.978876

For instance, 31–40 year olds, have an average value of y that is 2.11 higher than that of 20–30 year olds, controlling for the other covariates in the model.

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To learn more about factor variables, see the manual entry.

To learn more about pwcompare, see its manual entry.

Back to highlights

See New in Stata 18 to learn about what was added in Stata 18.