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SV: st: Cross-level interaction with xtmixed


From   Marc Peters <marcpeters32c@yahoo.se>
To   "statalist@hsphsun2.harvard.edu" <statalist@hsphsun2.harvard.edu>
Subject   SV: st: Cross-level interaction with xtmixed
Date   Tue, 10 Jan 2012 17:30:39 +0000 (GMT)

Thank you Joerg, 


Of course, Here are the outputs. 


*MODEL 1, MAIN MODEL WITH RANDOM INTERCEPTS 



. xtmixed depvar indepvar1 indepvar2 indepvar3 indepvar4 ||id:, var 

Performing EM optimization: 

Performing gradient-based optimization: 

Iteration 0:   log likelihood =  1403.7234  
Iteration 1:   log likelihood =  1403.7234  

Computing standard errors:

Mixed-effects ML regression                     Number of obs      =      2030
Group variable: id                              Number of groups   =        12

                                                Obs per group: min =       136
                                                               avg =     169.2
                                                               max =       223


                                                Wald chi2(4)       =     36.76
Log likelihood =  1403.7234                     Prob > chi2        =    0.0000

------------------------------------------------------------------------------
      depvar |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   indepvar1 |   .0756643   .0146331     5.17   0.000      .046984    .1043447
   indepvar2 |   .0374981   .0275734     1.36   0.174    -.0165447     .091541
   indepvar3 |   .0274294    .013366     2.05   0.040     .0012325    .0536264
   indepvar4 |   -.023015   .0162651    -1.41   0.157    -.0548941    .0088641
       _cons |   .1835104   .0813048     2.26   0.024      .024156    .3428648
------------------------------------------------------------------------------


*MODEL 2, WITH INTERACTION



. xtmixed depvar c.indepvar1##c.indepvar2 indepvar3 indepvar4 ||id: indepvar1, var cov(un)

Performing EM optimization: 

Performing gradient-based optimization: 

Iteration 0:   log likelihood =  1471.8551  
Iteration 1:   log likelihood =  1471.8551  

Computing standard errors:

Mixed-effects ML regression                     Number of obs      =      2030
Group variable: id                              Number of groups   =        12

                                                Obs per group: min =       136
                                                               avg =     169.2
                                                               max =       223


                                                Wald chi2(5)       =      8.32
Log likelihood =  1471.8551                     Prob > chi2        =    0.1394

-----------------------------------------------------------------------------------------
                 depvar |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------------+----------------------------------------------------------------
              indepvar1 |   .1564069   .1446754     1.08   0.280    -.1271518    .4399656
              indepvar2 |   .0379993   .0504303     0.75   0.451    -.0608422    .1368408
                        |
c.indepvar1#c.indepvar2 |   -.030349   .0597786    -0.51   0.612    -.1475128    .0868149
                        |
              indepvar3 |   .0277277   .0130601     2.12   0.034     .0021305     .053325
              indepvar4 |    .004068   .0164534     0.25   0.805    -.0281802    .0363161
                  _cons |   .0286898    .130898     0.22   0.827    -.2278655    .2852452
-----------------------------------------------------------------------------------------

------------------------------------------------------------------------------
  Random-effects Parameters  |   Estimate   Std. Err.     [95% Conf. Interval]
-----------------------------+------------------------------------------------
id: Unstructured             |
               var(indepv~1) |   .0333422   .0147683      .0139949    .0794363
                  var(_cons) |   .0217169     .00978      .0089839    .0524964
         cov(indepv~1,_cons) |  -.0246262   .0114631     -.0470934   -.0021589
-----------------------------+------------------------------------------------
               var(Residual) |    .013216   .0004171      .0124232    .0140594
------------------------------------------------------------------------------
LR test vs. linear regression:       chi2(3) =   495.38   Prob > chi2 = 0.0000

Note: LR test is conservative and provided only for reference.



*MARGINAL EFFECTS


. margins, dydx(indepvar1) at (indepvar2=(1(1) 4)) vsquish post

Average marginal effects                          Number of obs   =       2030

Expression   : Linear prediction, fixed portion, predict()
dy/dx w.r.t. : indepvar1
1._at        : indepvar2       =           1
2._at        : indepvar2       =           2
3._at        : indepvar2       =           3
4._at        : indepvar2       =           4

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
indepvar1    |
         _at |
          1  |   .1260579   .0922453     1.37   0.172    -.0547396    .3068555
          2  |    .095709   .0568716     1.68   0.092    -.0157572    .2071752
          3  |     .06536   .0714598     0.91   0.360    -.0746986    .2054186
          4  |    .035011   .1188511     0.29   0.768    -.1979328    .2679549
------------------------------------------------------------------------------



Thank you so much for your help!


Best, 

Marc



----- Ursprungligt meddelande ----
Från: Joerg Luedicke <joerg.luedicke@gmail.com>
Till: statalist@hsphsun2.harvard.edu
Kopia: 
Skickat: tisdag, 10 januari 2012 16:57
Ämne: Re: st: Cross-level interaction with xtmixed

Perhaps it would be helpful if you could post the output of the two
models plus output from your marginal effects calculations.

J.

On Tue, Jan 10, 2012 at 7:34 AM, Marc Peters <marcpeters32c@yahoo.se> wrote:
> I really don't want to bug you with this question. But if anyone know anything about this I would appreciate your help tremendously.
> Please don't hesitate to ask me if you need more information.
>
> Best,
>
> Marc
>
>
> ----- Ursprungligt meddelande ----
> Från: Marc Peters <marcpeters32c@yahoo.se>
> Till: "statalist@hsphsun2.harvard.edu" <statalist@hsphsun2.harvard.edu>
> Kopia:
> Skickat: måndag, 9 januari 2012 10:20
> Ämne: st: Cross-level interaction with xtmixed
>
> Dear all,
>
> I am trying to run a multi-level OLS-regression with a cross-level interaction. My main model uses random intercepts (but not random slopes).
>
> I use the xtmixed command in Stata:
>
> xtmixed depvar indepvar1 indepvar2 indepvar3 indepvar|| groupvar:, var
>
> In this model indepvar1 (continuous level-1 variable) becomes highly significant. Indepvar1 is the only level-1 variable in the model. I would like to see whether the effect of indepvar1 decreases if interacted with indepvar2 (a continuous level-2 variable with only four observed values). Since this is a cross-level interaction I need to sepcify a random slope model:
>
> xtmixed depvar indepvar1 indepvar2 indepvar1*indepvar2 indepvar3 indepvar4|| groupvar: indepvar1, cov(un)
>
> In this model both indepvar1 and indepvar1*indepvar2  becomes insignificant. When using the margins command to see the marginal effect of indepvar1 at the different levels of indepvar2, indepvar1 is insignificant at every level of indepvar2. I have a porblem understanding why this is or how to interpret these results.
>
> Rerunning model 1 with random slopes (but with no interactions) indepvar1 also gets insignificant. Is there a problem with how I have specified the models or how should I understand the results? Why is a variable insignificant with random slopes, but significant without them?
>
> Best,
>
> Marc
>
>
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