st: dropped interaction term in post hoc probing of significant moderation effect using svy regress

[Ishtar Govia <ishtargovia@gmail.com>]

Dear List,

I am using Stata 10/SE 10.1 for Macs. I have a question concerning a  
dropped interaction term. I am using survey data so I use the -svy-  
commands

I am doing post hoc probing of a significant interaction effect using  
the Aiken & West 1991 simple slopes method, as delineated in Holmbeck  
2002. I created a two new conditional variables based on the centered  
moderator (high and low emotional support, variables hiemotspfm and  
loemotspfm respectively) and then computed interactions of each of  
those new conditional variables with the independent variable (the  
variable for the interaction with the high emotional support and IV  
interaction below is called hiemot_subtle). I then attempted to run 2  
post-hoc multiple linear regressions, each including the IV main  
effect, one of the conditional variables, and the interaction of the  
IV and conditional moderator variable.

However, I was stumped in running the first of those two post hoc  
regressions. When I ran the following, however, the interaction term  
was dropped. I have tried to run the regression with only the new  
conditional moderator main effect and it works. However, when I try to  
run the regression with only the interaction between the new  
conditional moderator and the IV main effect, it still drops the  
interaction. Does anyone have any ideas why this might have occurred,  
and more importantly if/how I can make the regression run without  
having the interaction dropped?

Thanks for your consideration,

Ishtar Govia
ishtargovia@gmail.com

. svy, subpop (s2subpop): regress log_kessler6_sum age female hhinc  
forgnbrn haitian sepdivwid nevmarr otherstate outofusa C_subtle_avg  
hiemotspfm hiemot_subtle if !missing(age, female, hhinc, forgnbrn,  
haitian, sepdivwid, nevmarr, otherstate, outofusa, C_subtle_avg,  
C_emotspfm, subtle_emot)
(running regress on estimation sample)

Survey: Linear regression

Number of strata   =        19                  Number of obs       
=       550
Number of PSUs     =        44                  Population size    =  
64.977893
                                                Subpop. no. of obs  
=       550
                                                Subpop. size       =  
64.977893
                                                Design df           
=        25
                                                F(  11,     15)     
=         .
                                                Prob > F            
=         .
                                                R-squared           
=    0.2971

------------------------------------------------------------------------------
             |             Linearized
log_kessle~m |      Coef.   Std. Err.      t    P>|t|     [95% Conf.  
Interval]
------------- 
+----------------------------------------------------------------
         age |  -.0012483   .0018973    -0.66   0.517    -. 
0051559    .0026592
      female |  -.1549944   .0492037    -3.15   0.004    -.2563314    
-.0536575
       hhinc |  -1.88e-06   6.11e-07    -3.08   0.005    -3.14e-06    
-6.22e-07
    forgnbrn |  -.0346815   .0702836    -0.49   0.626    -. 
1794334    .1100703
     haitian |   .0294622   .0609838     0.48   0.633    -. 
0961362    .1550606
   sepdivwid |   .1145513   .0839624     1.36   0.185    -. 
0583725     .287475
     nevmarr |   .1836943   .0487229     3.77   0.001     . 
0833476     .284041
  otherstate |  -.0144171   .0655521    -0.22   0.828    -. 
1494241    .1205899
    outofusa |   .0085318   .0553012     0.15   0.879    -. 
1053631    .1224266
C_subtle_avg |   .1138018   .0358374     3.18   0.004     .0399933    . 
1876103
  hiemotspfm |  -.0892076   .0468532    -1.90   0.068    -. 
1857036    .0072884
hiemot_sub~e |  (dropped)
       _cons |    2.25171   .1138259    19.78   0.000     2.017281     
2.486139
------------------------------------------------------------------------------




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