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NOTE: This page is under construction.
Table 5.2, page 145
Table 5.2 uses data file reading.dat. Note the format of this file, that it is in wide form. Here is a display of the first 10 observations.
. list id cage1 cage2 cage3 cagegrp1 cagegrp2 cagegrp3 piat1 piat2 piat3 +-----------------------------------------------------------------------------------------------+ | id cage1 cage2 cage3 cagegrp1 cagegrp2 cagegrp3 piat1 piat2 piat3 | |-----------------------------------------------------------------------------------------------| | 1 -.5 1.833333 3.833333 0 2 4 18 35 59 | | 2 -.5 2 4.083333 0 2 4 18 25 28 | | 3 -.4166665 1.916667 3.916667 0 2 4 18 23 32 | | 4 -.5 2 4.166667 0 2 4 18 31 50 | | 5 -.5 1.666667 3.75 0 2 4 18 33 53 | |-----------------------------------------------------------------------------------------------| | 6 -.5 2 4 0 2 4 18 28 31 | | 7 -.4166665 2 4 0 2 4 17 28 28 | | 8 -.5 1.916667 4.083333 0 2 4 17 29 41 | | 9 -.3333335 2.25 4.333333 0 2 4 28 26 26 | | 10 -.3333335 1.916667 3.916667 0 2 4 16 20 21 | |-----------------------------------------------------------------------------------------------|
Model A: Using AGEGRPi-6.5 as a temporal predictor, called cagegrpi (i.e., cagegrp1 cagegrp2 cagegrp3). These were created before making the data file.
Title:
Table 5.2, Model A.
Data:
File is c:\alda\reading.dat ;
Variable:
Names are
id agegrp1 agegrp2 agegrp3 age1 age2 age3 piat1 piat2 piat3 cage1
cage2 cage3 cagegrp1 cagegrp2 cagegrp3;
Missing are all (-999999999) ;
Usevariables are
piat1 piat2 piat3 cagegrp1 cagegrp2 cagegrp3;
Tscores cagegrp1-cagegrp3 ;
Analysis:
Type = random ;
estimator = ml;
Model:
i s | piat1-piat3 at cagegrp1-cagegrp3 ;
i with s;
piat1-piat3 (1) ;
------------------------------------------------------------------------------------------------
TESTS OF MODEL FIT
Loglikelihood
H0 Value -909.978
Information Criteria
Number of Free Parameters 6
Akaike (AIC) 1831.956
Bayesian (BIC) 1846.888
Sample-Size Adjusted BIC 1827.953
(n* = (n + 2) / 24)
MODEL RESULTS
Estimates S.E. Est./S.E.
I WITH
S 1.567 2.070 0.757
Means
I 21.133 0.617 34.273
S 5.039 0.295 17.065
Intercepts
PIAT1 0.000 0.000 0.000
PIAT2 0.000 0.000 0.000
PIAT3 0.000 0.000 0.000
Variances
I 11.368 6.115 1.859
S 4.388 1.265 3.470
Residual Variances
PIAT1 26.961 4.029 6.691
PIAT2 26.961 4.029 6.691
PIAT3 26.961 4.029 6.691
Note that the residual variances are constrained to be equal. See the Supplemental Analyses for Chapter 5 to see an example where the residual variances a permitted to freely vary.
Model B: Using AGE-6.5 as a temporal predictor, i.e., cage1 cage2 cage3.
Title:
Data:
File is c:\alda\reading.dat ;
Variable:
Names are
id agegrp1 agegrp2 agegrp3 age1 age2 age3 piat1 piat2 piat3 cage1
cage2 cage3 cagegrp1 cagegrp2 cagegrp3;
Missing are all (-999999999) ;
Usevariables are
piat1 piat2 piat3 cage1 cage2 cage3;
Tscores cage1-cage3 ;
Analysis:
Type = random;
estimator = ml;
MODEL:
i s | piat1-piat3 at cage1-cage3 ;
i with s;
piat1-piat3 (1) ;
------------------------------------------------------------------------------------------------
TESTS OF MODEL FIT
Loglikelihood
H0 Value -901.960
Information Criteria
Number of Free Parameters 6
Akaike (AIC) 1815.920
Bayesian (BIC) 1830.851
Sample-Size Adjusted BIC 1811.916
(n* = (n + 2) / 24)
MODEL RESULTS
Estimates S.E. Est./S.E.
I WITH
S 2.139 1.814 1.179
Means
I 21.033 0.564 37.285
S 4.549 0.262 17.397
Intercepts
PIAT1 0.000 0.000 0.000
PIAT2 0.000 0.000 0.000
PIAT3 0.000 0.000 0.000
Variances
I 5.910 6.045 0.978
S 3.384 1.019 3.321
Residual Variances
PIAT1 27.009 4.232 6.382
PIAT2 27.009 4.232 6.382
PIAT3 27.009 4.232 6.382
Table 5.7, page 163
Table 5.7
Model A: Initial growth model, using Person (wide) unemp.dat data file.
Title:
Table 5.7, Model A, Person (wide) file
Data:
File is c:\alda\unemp.dat ;
Variable:
Names are
id cesd1 cesd2 cesd3 months1 months2 months3 unemp1 unemp2 unemp3
ubym1 ubym2 ubym3;
Missing are all (-999999999) ;
Usevariables are
cesd1 cesd2 cesd3 months1 months2 months3 ;
Tscores months1-months3 ;
Analysis:
Type = random;
estimator = ml;
MODEL:
i s | cesd1-cesd3 at months1-months3 ;
i with s;
cesd1-cesd3 (1) ;
-----------------------------------------------------------------------------
TESTS OF MODEL FIT
Loglikelihood
H0 Value -2189.403
Information Criteria
Number of Free Parameters 6
Akaike (AIC) 4390.806
Bayesian (BIC) 4410.382
Sample-Size Adjusted BIC 4391.375
(n* = (n + 2) / 24)
MODEL RESULTS
Estimates S.E. Est./S.E.
I WITH
S -2.335 1.329 -1.757
Means
I 17.187 0.845 20.332
S -0.387 0.085 -4.533
Variances
I 78.573 15.179 5.176
S 0.325 0.176 1.850
Residual Variances
CESD1 66.008 6.574 10.041
CESD2 66.008 6.574 10.041
CESD3 66.008 6.574 10.041
Model A (again): Initial growth model, using person period ("long") unemp_pp.dat data file.
Title:
Table 5.7, Model A, Person Period (long) file
Data:
File is c:\alda\unemp_pp.dat ;
Variable:
Names are
id months cesd unemp;
Missing are all (-999999999) ;
Usevariables are
months cesd ;
cluster = id;
within = months ;
Analysis:
Type = random twolevel ;
mconvergence = .000001;
estimator = ml;
model:
%within%
s | cesd on months;
%between%
cesd with s;
-----------------------------------------------------------------------------
TESTS OF MODEL FIT
Loglikelihood
H0 Value -2566.569
Information Criteria
Number of Free Parameters 6
Akaike (AIC) 5145.137
Bayesian (BIC) 5172.217
Sample-Size Adjusted BIC 5153.166
(n* = (n + 2) / 24)
MODEL RESULTS
Estimates S.E. Est./S.E.
Within Level
Residual Variances
CESD 68.848 6.602 10.428
Between Level
CESD WITH
S -3.058 1.385 -2.208
Means
CESD 17.669 0.776 22.782
S -0.422 0.083 -5.083
Variances
CESD 86.852 14.963 5.804
S 0.355 0.184 1.925
Model B: Main effect of unemployment using person period ("long") unemp_pp.dat data file.
Title:
Table 5.7, Model B, Person Period (long) file
Data:
File is c:\alda\unemp_pp.dat ;
Variable:
Names are
id months cesd unemp;
Missing are all (-999999999) ;
Usevariables are
months cesd unemp;
cluster = id;
within = months unemp;
Analysis:
Type = random missing twolevel ;
mconvergence = .000001;
estimator = ml;
model:
%within%
cesd on unemp;
s | cesd on months;
%between%
cesd with s;
-----------------------------------------------------------------------------
TESTS OF MODEL FIT
Loglikelihood
H0 Value -2553.802
Information Criteria
Number of Free Parameters 7
Akaike (AIC) 5121.603
Bayesian (BIC) 5153.196
Sample-Size Adjusted BIC 5130.970
(n* = (n + 2) / 24)
MODEL RESULTS
Estimates S.E. Est./S.E.
Within Level
CESD ON
UNEMP 5.111 0.996 5.133
Residual Variances
CESD 62.388 6.013 10.375
Between Level
CESD WITH
S -3.894 1.370 -2.842
Means
CESD 12.666 1.247 10.157
S -0.202 0.093 -2.163
Variances
CESD 93.518 14.820 6.310
S 0.465 0.180 2.585
Model C: Effect of unemployment on initial status and growth rate.
Title:
Table 5.7, Model C, Person Period (long) file
Data:
File is H:\alda\mplus\unemployment_pp.dat ;
Define:
monBYun = months * unemp;
Variable:
Names are
id months cesd unemp;
Missing are all (-999999999) ;
Usevariables are
months cesd unemp monBYun;
cluster = id;
within = months unemp monBYun;
Analysis:
Type = random missing twolevel ;
mconvergence = .000001;
estimator = ml;
model:
%within%
cesd on unemp monBYun;
s | cesd on months;
%between%
cesd with s;
-----------------------------------------------------------------------------
TESTS OF MODEL FIT
Loglikelihood
H0 Value -2551.523
Information Criteria
Number of Free Parameters 8
Akaike (AIC) 5119.047
Bayesian (BIC) 5155.153
Sample-Size Adjusted BIC 5129.752
(n* = (n + 2) / 24)
MODEL RESULTS
Estimates S.E. Est./S.E.
Within Level
CESD ON
UNEMP 8.529 1.880 4.538
MONBYUN -0.465 0.217 -2.140
Residual Variances
CESD 62.031 5.966 10.398
Between Level
CESD WITH
S -3.873 1.359 -2.850
Means
CESD 9.617 1.891 5.086
S 0.162 0.194 0.836
Variances
CESD 93.712 14.777 6.342
S 0.451 0.177 2.544
Model D: Allowing unemployment to have both fix and random effects. There are only 193 groups that have all data points for the three time points.
Skipped for now.
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