Table 4.1 on pages 94-95.
Model A.
Data: File is alcohol1_pp.dat ; Variable: Names are id age coa male age_14 alcuse peer cpeer ccoa; Missing are all (-9999) ; usevariables are alcuse; cluster is id; Analysis: type = random twolevel; model: %within% alcuse;
Loglikelihood
H0 Value -335.078
H0 Scaling Correction Factor 1.167
for MLR
H1 Value -335.078
H1 Scaling Correction Factor 1.167
for MLR
Information Criteria
Number of Free Parameters 3
Akaike (AIC) 676.156
Bayesian (BIC) 686.672
Sample-Size Adjusted BIC 677.162
(n* = (n + 2) / 24)
RMSEA (Root Mean Square Error Of Approximation)
Estimate 0.000
SRMR (Standardized Root Mean Square Residual)
Value for Within 0.000
Value for Between 0.000
MODEL RESULTS
Two-Tailed
Estimate S.E. Est./S.E. P-Value
Within Level
Variances
ALCUSE 0.562 0.083 6.787 0.000
Between Level
Means
ALCUSE 0.922 0.096 9.633 0.000
Variances
ALCUSE 0.564 0.106 5.315 0.000
Model B.
Data:
File is alcohol1_pp.dat ;
Variable:
Names are id age coa male age_14 alcuse peer cpeer ccoa;
Missing are all (-9999) ;
usevariables are alcuse age_14;
within is age_14;
cluster is id;
Analysis: type = random twolevel;
model:
%within%
s| alcuse on age_14;
%between%
s alcuse;
s with alcuse;
TESTS OF MODEL FIT
Loglikelihood
H0 Value -318.306
H0 Scaling Correction Factor 1.119
for MLR
Information Criteria
Number of Free Parameters 6
Akaike (AIC) 648.611
Bayesian (BIC) 669.643
Sample-Size Adjusted BIC 650.623
(n* = (n + 2) / 24)
MODEL RESULTS
Two-Tailed
Estimate S.E. Est./S.E. P-Value
Within Level
Residual Variances
ALCUSE 0.337 0.066 5.106 0.000
Between Level
S WITH
ALCUSE -0.069 0.074 -0.921 0.357
Means
ALCUSE 0.651 0.105 6.198 0.000
S 0.271 0.062 4.334 0.000
Variances
ALCUSE 0.625 0.162 3.861 0.000
S 0.151 0.056 2.722 0.006
Model C.
Data:
File is alcohol1_pp.dat ;
Variable:
Names are id age coa male age_14 alcuse peer cpeer ccoa;
Missing are all (-9999) ;
usevariables are alcuse age_14 coa coaxage;
within is age_14 coaxage;
between is coa;
cluster is id;
define: coaxage = coa*age_14;
Analysis: type = random twolevel;
estimator = ml;
model:
%within%
s| alcuse on age_14;
alcuse on coaxage;
%between%
alcuse on coa;
s alcuse;
s with alcuse;
TESTS OF MODEL FIT
Loglikelihood
H0 Value -310.601
Information Criteria
Number of Free Parameters 8
Akaike (AIC) 637.203
Bayesian (BIC) 665.245
Sample-Size Adjusted BIC 639.886
(n* = (n + 2) / 24)
MODEL RESULTS
Two-Tailed
Estimate S.E. Est./S.E. P-Value
Within Level
ALCUSE ON
COAXAGE -0.050 0.125 -0.398 0.690
Residual Variances
ALCUSE 0.337 0.053 6.403 0.000
Between Level
ALCUSE ON
COA 0.744 0.195 3.822 0.000
S WITH
ALCUSE -0.059 0.066 -0.903 0.366
Means
S 0.293 0.084 3.482 0.000
Intercepts
ALCUSE 0.316 0.131 2.416 0.016
Variances
S 0.151 0.056 2.671 0.008
Residual Variances
ALCUSE 0.488 0.128 3.815 0.000
Model D.
Data:
File is alcohol1_pp.dat ;
Variable:
Names are id age coa male age_14 alcuse peer cpeer ccoa;
Missing are all (-9999) ;
usevariables are alcuse age_14 coa peer coaxage pbyage;
within is age_14 coaxage pbyage;
between is coa peer;
cluster is id;
define: coaxage = coa*age_14;
pbyage = peer*age_14;
Analysis: type = random twolevel;
estimator = ml;
model:
%within%
s| alcuse on age_14;
alcuse on coaxage pbyage;
%between%
alcuse on coa peer;
s alcuse;
s with alcuse;
TESTS OF MODEL FIT
Loglikelihood
H0 Value -294.345
Information Criteria
Number of Free Parameters 10
Akaike (AIC) 608.691
Bayesian (BIC) 643.744
Sample-Size Adjusted BIC 612.044
(n* = (n + 2) / 24)
MODEL RESULTS
Two-Tailed
Estimate S.E. Est./S.E. P-Value
Within Level
ALCUSE ON
COAXAGE -0.014 0.125 -0.112 0.911
PBYAGE -0.150 0.086 -1.749 0.080
Residual Variances
ALCUSE 0.337 0.053 6.403 0.000
Between Level
ALCUSE ON
COA 0.579 0.162 3.564 0.000
PEER 0.694 0.112 6.225 0.000
S WITH
ALCUSE -0.006 0.055 -0.111 0.911
Means
S 0.429 0.114 3.777 0.000
Intercepts
ALCUSE -0.317 0.148 -2.138 0.033
Variances
S 0.139 0.055 2.538 0.011
Residual Variances
ALCUSE 0.241 0.093 2.602 0.009
Model E.
Data:
File is alcohol1_pp.dat ;
Variable:
Names are id age coa male age_14 alcuse peer cpeer ccoa;
Missing are all (-9999) ;
usevariables are alcuse age_14 coa peer pbyage;
within is age_14 pbyage;
between is coa peer;
cluster is id;
define: pbyage = peer*age_14;
Analysis: type = random twolevel;
estimator = ml;
model:
%within%
s| alcuse on age_14;
alcuse on pbyage;
%between%
alcuse on coa peer;
s alcuse;
s with alcuse;
TESTS OF MODEL FIT
Loglikelihood
H0 Value -294.352
Information Criteria
Number of Free Parameters 9
Akaike (AIC) 606.703
Bayesian (BIC) 638.251
Sample-Size Adjusted BIC 609.722
(n* = (n + 2) / 24)
MODEL RESULTS
Two-Tailed
Estimate S.E. Est./S.E. P-Value
Within Level
ALCUSE ON
PBYAGE -0.151 0.085 -1.791 0.073
Residual Variances
ALCUSE 0.337 0.053 6.403 0.000
Between Level
ALCUSE ON
COA 0.571 0.146 3.906 0.000
PEER 0.695 0.111 6.248 0.000
S WITH
ALCUSE -0.006 0.055 -0.111 0.911
Means
S 0.425 0.106 4.022 0.000
Intercepts
ALCUSE -0.314 0.146 -2.148 0.032
Variances
S 0.139 0.055 2.539 0.011
Residual Variances
ALCUSE 0.241 0.093 2.602 0.009
Model F.
Data:
File is alcohol1_pp.dat ;
Variable:
Names are id age coa male age_14 alcuse peer cpeer ccoa;
Missing are all (-9999) ;
usevariables are alcuse age_14 coa cpeer cpbyage;
within is age_14 cpbyage;
between is coa cpeer;
cluster is id;
define: cpbyage = cpeer*age_14;
Analysis: type = random twolevel;
estimator = ml;
model:
%within%
s| alcuse on age_14;
alcuse on cpbyage;
%between%
alcuse on coa cpeer;
s alcuse;
s with alcuse;
TESTS OF MODEL FIT
Loglikelihood
H0 Value -294.352
Information Criteria
Number of Free Parameters 9
Akaike (AIC) 606.703
Bayesian (BIC) 638.251
Sample-Size Adjusted BIC 609.722
(n* = (n + 2) / 24)
MODEL RESULTS
Two-Tailed
Estimate S.E. Est./S.E. P-Value
Within Level
ALCUSE ON
CPBYAGE -0.151 0.085 -1.792 0.073
Residual Variances
ALCUSE 0.337 0.053 6.401 0.000
Between Level
ALCUSE ON
COA 0.571 0.146 3.906 0.000
CPEER 0.695 0.111 6.249 0.000
S WITH
ALCUSE -0.006 0.055 -0.109 0.913
Means
S 0.271 0.061 4.417 0.000
Intercepts
ALCUSE 0.394 0.104 3.804 0.000
Variances
S 0.139 0.055 2.537 0.011
Residual Variances
ALCUSE 0.241 0.093 2.600 0.009
Model G.
Data:
File is alcohol1_pp.dat ;
Variable:
Names are id age coa male age_14 alcuse peer cpeer ccoa;
Missing are all (-9999) ;
usevariables are alcuse age_14 ccoa cpeer cpbyage;
within is age_14 cpbyage;
between is ccoa cpeer;
cluster is id;
define: cpbyage = cpeer*age_14;
Analysis: type = random twolevel;
estimator = ml;
model:
%within%
s| alcuse on age_14;
alcuse on cpbyage;
%between%
alcuse on ccoa cpeer;
s alcuse;
s with alcuse;
TESTS OF MODEL FIT
Loglikelihood
H0 Value -294.352
Information Criteria
Number of Free Parameters 9
Akaike (AIC) 606.703
Bayesian (BIC) 638.251
Sample-Size Adjusted BIC 609.722
(n* = (n + 2) / 24)
MODEL RESULTS
Two-Tailed
Estimate S.E. Est./S.E. P-Value
Within Level
ALCUSE ON
CPBYAGE -0.151 0.085 -1.792 0.073
Residual Variances
ALCUSE 0.337 0.053 6.401 0.000
Between Level
ALCUSE ON
CCOA 0.571 0.146 3.906 0.000
CPEER 0.695 0.111 6.249 0.000
S WITH
ALCUSE -0.006 0.055 -0.109 0.913
Means
S 0.271 0.061 4.417 0.000
Intercepts
ALCUSE 0.651 0.080 8.166 0.000
Variances
S 0.139 0.055 2.537 0.011
Residual Variances
ALCUSE 0.241 0.093 2.600 0.009
Test of equation 4.18 on page 123 using model F.
Data:
File is alcohol1_pp.dat ;
Variable:
Names are id age coa male age_14 alcuse peer cpeer ccoa;
Missing are all (-9999) ;
usevariables are alcuse age_14 coa cpeer cpbyage;
within is age_14 cpbyage;
between is coa cpeer;
cluster is id;
define: cpbyage = cpeer*age_14;
Analysis: type = random twolevel;
estimator = ml;
model:
%within%
s| alcuse on age_14;
alcuse on cpbyage;
%between%
alcuse on coa cpeer;
s alcuse;
s with alcuse;
[alcuse] (p1);
[s] (p2);
model test:
p1 = 0;
p2 = 0;
TESTS OF MODEL FIT
Wald Test of Parameter Constraints
Value 51.029
Degrees of Freedom 2
P-Value 0.0000
[more output omitted]