-------------------------------------------------------------------------------
      name:  <unnamed>
       log:  Q:\C-modelling\runmlwin\website\logfiles\2020-03-27\16\5.2.smcl
  log type:  smcl
 opened on:  27 Mar 2020, 18:21:30

. ****************************************************************************
. * Module 5: Introduction to Multilevel Modelling Stata Practicals
. *
. *     P5.2: Adding Student-level Explanatory Variables: Random Intercept
. *           Models
. *
. *           George Leckie
. *           Centre for Multilevel Modelling, 2010
. ****************************************************************************
. *     Stata do-file to replicate all analyses using runmlwin
. *
. *     George Leckie
. *     Centre for Multilevel Modelling, 2013
. *     http://www.bristol.ac.uk/cmm/software/runmlwin/
. ****************************************************************************
. 
. 
. use "http://www.bristol.ac.uk/cmm/media/runmlwin/5.2.dta", clear

. 
. runmlwin score cons cohort90, ///
>         level2(schoolid: cons) ///
>         level1(caseid: cons) ///
>         nopause
 
MLwiN 3.05 multilevel model                     Number of obs      =     33988
Normal response model (hierarchical)
Estimation algorithm: IGLS

-----------------------------------------------------------
                |   No. of       Observations per Group
 Level Variable |   Groups    Minimum    Average    Maximum
----------------+------------------------------------------
       schoolid |      508          1       66.9        190
-----------------------------------------------------------

Run time (seconds)   =       0.88
Number of iterations =          5
Log likelihood       = -140456.79
Deviance             =  280913.58
------------------------------------------------------------------------------
       score |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        cons |   30.55914    .322513   94.75    0.000     29.92703    31.19125
    cohort90 |   1.214956   .0155293   78.24    0.000     1.184519    1.245393
------------------------------------------------------------------------------

------------------------------------------------------------------------------
   Random-effects Parameters |   Estimate   Std. Err.     [95% Conf. Interval]
-----------------------------+------------------------------------------------
Level 2: schoolid            |
                   var(cons) |   45.97869   3.255724      39.59759    52.35979
-----------------------------+------------------------------------------------
Level 1: caseid              |
                   var(cons) |   219.2885   1.694483      215.9673    222.6096
------------------------------------------------------------------------------

. 
. predict predscore

. 
. egen pickone = tag(schoolid cohort90)

. 
. sort schoolid cohort90

. 
. twoway connected predscore cohort90 if pickone==1, connect(ascending)

. 
. generate multiplecohorts = pickone

. 
. bysort schoolid (cohort90): replace multiplecohorts = 0 ///
>         if cohort90[_N]==cohort90[1]
(32 real changes made)

. 
. twoway connected predscore cohort90 if multiplecohorts==1, connect(ascending)

. 
end of do-file