How to output random-intercepts and random-slope information to Excel properly?

[Maisha Huq]

Hi All,

My question: Would you know why the random-effects portions of my (i) random-intercept only and (ii) random-intercept & random-slope models' RESULTS are not outputting to Excel properly?

Version is STATA15

Some background:

• This is an unbalanced longitudinal dataset for K-8 students, where each student can have either 2 or 3 years of data. The person-unit variable is record_id (unique to each student) and the time variable is year (values: 1, 2, 3). In the long dataset, n=4581
• I ran a random-effects model assessing the longitudinal association between proportion of absences (variable name: absenty_p, it is a continuous variable) and the child's mean BMI (variable name: bmim). Although BMI is an age and gender-adjusted measure, I wanted to see if random intercepts by the grade-levels (variable name: grade, values: 0-8) are statistically significant or not. I also wanted to see if the random slopes by grade are stat sig or not. So, I ran the following model:
• RANDOM INT-ONLY: xi: mixed: absenty_p bmim || grade:
• RANDOM INT & RAND SLOPE: xi: mixed: absenty_p bmim || grade: bmim

Directly below are the STATA results. Then, below that is what the results look like in Excel. The random-effects results don't match. Would you know why the random-effects results do not output to Excel properly?

eststo clear
xi: mixed absenty_p bmim || grade:
esttab using "/Users/maishahuq/Desktop/BMI-A-A/MLM_bmi_absent_grade.csv", replace se ar2


Performing EM optimization:

Performing gradient-based optimization:

Iteration 0:   log likelihood =  3895.8906  
Iteration 1:   log likelihood =  3895.8906  

Computing standard errors:

Mixed-effects ML regression                     Number of obs     =      2,676
Group variable: grade                           Number of groups  =          9

                                                Obs per group:
                                                              min =        225
                                                              avg =      297.3
                                                              max =        343

                                                Wald chi2(1)      =       0.08
Log likelihood =  3895.8906                     Prob > chi2       =     0.7762

------------------------------------------------------------------------------
   absenty_p |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        bmim |   .0000584   .0002054     0.28   0.776    -.0003441    .0004609
       _cons |   .0564974   .0050627    11.16   0.000     .0465748    .0664201
------------------------------------------------------------------------------

------------------------------------------------------------------------------
  Random-effects Parameters  |   Estimate   Std. Err.     [95% Conf. Interval]
-----------------------------+------------------------------------------------
grade: Identity              |
                  var(_cons) |   .0000597   .0000336      .0000198      .00018
-----------------------------+------------------------------------------------
               var(Residual) |   .0031639   .0000866      .0029986    .0033384
------------------------------------------------------------------------------
LR test vs. linear model: chibar2(01) = 29.82         Prob >= chibar2 = 0.0000

	(1)
	absenty_p
absenty_p
bmim	0.0000584
	(0.000205)
_cons	0.0565***
	(0.00506)
lns1_1_1
_cons	-4.863***
	(0.282)
lnsig_e
_cons	-2.878***
	(0.0137)
N	2676
adj. R-sq
Standard errors in parentheses
="* p<0.05	** p<0.01	*** p<0.001"