Hello,

My question is on estimating coefficients for random effects in mixed effect models. I am new to mixed effect models. One reviewer insisted on using mixed effect models with AIC and BIC results for my paper. I managed to do that so far. I am building a 3 level mixed effect model. The model includes 2367 students clustered within 93 classes which are clustered within 24 schools. I made 6 different models and the most complex one as follows;

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Fitting fixed-effects model:

Iteration 0:   log likelihood = -1363.5138  
Iteration 1:   log likelihood =  -1085.804  
Iteration 2:   log likelihood = -1059.1874  
Iteration 3:   log likelihood = -1058.9712  
Iteration 4:   log likelihood = -1058.9711  

Refining starting values:

Grid node 0:   log likelihood = -1056.6141

Fitting full model:

Iteration 0:   log likelihood = -1056.6141  (not concave)
Iteration 1:   log likelihood =   -1046.24  (not concave)
Iteration 2:   log likelihood = -1040.4289  
Iteration 3:   log likelihood = -1038.3414  
Iteration 4:   log likelihood = -1038.1282  
Iteration 5:   log likelihood = -1038.1257  
Iteration 6:   log likelihood = -1038.1257  

Mixed-effects ologit regression                 Number of obs     =      2,637

-------------------------------------------------------------
                |     No. of       Observations per Group
 Group Variable |     Groups    Minimum    Average    Maximum
----------------+--------------------------------------------
   school_num~r |         24         24      109.9        160
     class_code |         93         10       28.4         41
-------------------------------------------------------------

Integration method: mvaghermite                 Integration pts.  =          7

                                                Wald chi2(15)     =     332.82
Log likelihood = -1038.1257                     Prob > chi2       =     0.0000
----------------------------------------------------------------------------------------------
        __14_Going_to_Parks1 | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-----------------------------+----------------------------------------------------------------
              Age_Smaller_10 |   3.386472   1.080845     3.82   0.000     1.811646     6.33026
           Age_between_10_14 |   3.232854   .8032719     4.72   0.000     1.986492    5.261207
           Age_between_15_18 |          1  (omitted)
                GenderFemale |   1.157021   .1446025     1.17   0.243     .9056479    1.478165
  Involving Physical activity|   1.006707   .1186667     0.06   0.955      .799037    1.268351
            Overweight_Obeze |   .7651281   .1032076    -1.98   0.047      .587376    .9966716
       Mother_Father_Uni_Gra |   .8212992   .1248524    -1.30   0.195      .609682    1.106368
  Living_in_Gated_community1 |   1.769015   .3537469     2.85   0.004     1.195413     2.61785
             No parks around |    .192004   .0236017   -13.42   0.000     .1508961    .2443106
             Overall quality |   .2330365   .0336746   -10.08   0.000     .1755587    .3093326
 
-----------------------------+----------------------------------------------------------------
                       /cut1 |  -1.568161   .2280446    -6.88   0.000     -2.01512   -1.121202
-----------------------------+----------------------------------------------------------------
school_number                |
                   var(_cons)|   .1506769   .1114597                      .0353501     .642248
-----------------------------+----------------------------------------------------------------
school_number>class_code     |
                   var(_cons)|   .2778238   .1027409                      .1345829    .5735204
----------------------------------------------------------------------------------------------
LR test vs. ologit model: chi2(2) = 41.69                 Prob > chi2 = 0.0000

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

. estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
           . |      2,637         .  -1038.126      18    2112.251   2218.045
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.



I have a minor correction for the paper and she wants to see random effect estimates for all multilevel level regression models. However, as I am new to multi-effect models. I could not figure out how to get random effect estimates. I would appreciate help from experienced stata users.

Best Regards,