Example C3 Binary
response model
Raudenbush and Bhumirat
(1992) analysed data on children repeating a grade during their time at primary
school. The data were from a national survey of primary education in
Reference
Raudenbush, S.W., Bhumirat, C., 1992. The distribution of resources for
primary education and its consequences for educational achievement in
Data description
Number of observations (rows): 7185
Number of variables (columns): 4
The variables include the following:
schoolid =school identifier
sex= 1 if child is male, 0 otherwise
pped=1 if the child had pre primary experience, 0 otherwise
repeat=1 if the child repeated a grade during primary school, 0 otherwise
The first few lines of thaieduc1.dat look like
Sabre commands
out thaieduc.log
trace thaieduc.trace
data schoolid
sex pped repeat
read thaieduc1.dat
case schoolid
yvar
repeat
constant cons
lfit
cons
dis m
dis e
fit cons
dis m
dis e
data schoolid
sex pped repeat msesc
read thaieduc2.dat
case schoolid
yvar
repeat
constant cons
lfit msesc sex pped cons
dis m
dis e
fit msesc
sex pped cons
dis m
dis e
stop
Sabre log file
<S> trace thaieduc.trace
<S> data schoolid sex pped repeat
<S> read thaieduc1.dat
8582
observations in dataset
<S> case schoolid
<S> yvar repeat
<S> constant cons
<S> lfit cons
Iteration Log. lik.
Difference
__________________________________________
1 -5948.5891
2 -3625.9614 2323.
3 -3554.2465 71.71
4 -3553.4908 0.7557
5 -3553.4906 0.1283E-03
6 -3553.4906 0.1296E-09
<S> dis m
X-vars Y-var
______________________________
cons
repeat
Univariate model
Standard logit
Number of
observations = 8582
X-var df =
1
Log likelihood
= -3553.4906 on
8581 residual degrees of freedom
<S> dis e
Parameter Estimate Std. Err.
___________________________________________________
cons
-1.7738 0.30651E-01
<S> fit cons
Initial
Homogeneous Fit:
Iteration Log. lik.
Difference
__________________________________________
1 -5948.5891
2 -3625.9614 2323.
3 -3554.2465 71.71
4 -3553.4908 0.7557
5 -3553.4906 0.1283E-03
6 -3553.4906 0.1296E-09
Iteration Log. lik.
Step End-points Orthogonality
length 0 1
criterion
________________________________________________________________________
1 -3237.7286 1.0000 fixed fixed 334.98
2 -3219.2799 1.0000 fixed fixed 202.12
3 -3217.3207 1.0000 fixed fixed 167.22
4 -3217.2649 1.0000 fixed fixed 167.84
5 -3217.2642 1.0000 fixed fixed 216.57
6 -3217.2642 1.0000 fixed fixed 148.29
7
-3217.2642 1.0000 fixed fixed
<S> dis m
X-vars Y-var Case-var
________________________________________________
cons
repeat schoolid
Univariate model
Standard logit
Gaussian random
effects
Number of
observations = 8582
Number of
cases = 411
X-var df =
1
Scale df =
1
Log likelihood
= -3217.2642 on
8580 residual degrees of freedom
<S> dis e
Parameter Estimate Std. Err.
___________________________________________________
cons
-2.1263 0.79655E-01
scale
1.2984 0.84165E-01
<S> data schoolid sex pped repeat msesc
--- new analysis begins
<S> read thaieduc2.dat
7516
observations in dataset
<S> case schoolid
<S> yvar repeat
<S> constant cons
<S> lfit msesc sex pped cons
Iteration Log. lik.
Difference
__________________________________________
1 -5209.6942
2 -3094.0634 2116.
3 -3007.1924 86.87
4 -3004.7357 2.457
5 -3004.7313 0.4417E-02
6
-3004.7313 0.1669E-07
<S> dis m
X-vars Y-var
______________________________
cons
repeat
msesc
sex
pped
Univariate model
Standard logit
Number of
observations = 7516
X-var df =
4
Log likelihood
= -3004.7313 on
7512 residual degrees of freedom
<S> dis e
Parameter Estimate Std. Err.
___________________________________________________
cons -1.7832 0.58777E-01
msesc -0.24149 0.93750E-01
sex
0.42777 0.67637E-01
pped -0.56885 0.70421E-01
<S> fit msesc sex pped cons
Initial Homogeneous
Fit:
Iteration Log. lik.
Difference
__________________________________________
1 -5209.6942
2 -3094.0634 2116.
3 -3007.1924 86.87
4 -3004.7357 2.457
5 -3004.7313 0.4417E-02
6 -3004.7313 0.1669E-07
Iteration Log. lik.
Step End-points Orthogonality
length 0 1
criterion
________________________________________________________________________
1 -2741.1866 1.0000 fixed fixed 193.41
2 -2723.0976 1.0000 fixed fixed 178.19
3 -2720.8948 1.0000 fixed fixed 89.392
4 -2720.7670 1.0000 fixed fixed 40.916
5 -2720.7589 1.0000 fixed fixed 33.123
6 -2720.7582 1.0000 fixed fixed 23.011
7 -2720.7581 1.0000 fixed fixed
<S> dis m
X-vars Y-var Case-var
________________________________________________
cons
repeat schoolid
msesc
sex
pped
Univariate model
Standard logit
Gaussian random
effects
Number of
observations = 7516
Number of
cases = 356
X-var df =
4
Scale df =
1
Log likelihood
= -2720.7581
on 7511 residual degrees of
freedom
<S> dis e
Parameter Estimate Std. Err.
___________________________________________________
cons
-2.2280 0.10461
msesc -0.41370 0.22462
sex
0.53177 0.75805E-01
pped -0.64022 0.98885E-01
scale
1.3026 0.72601E-01
<S> stop