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:aldareading.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:aldareading.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 5.4, page 149, using the https://stats.idre.ucla.edu/wp-content/uploads/2016/02/ALDACh5Table5.4.txt data
We thank Hemant Kher for providing the Mplus code for this example.
Model A
Title: Table 5.4, Model A, Person (wide) file Data: File is "C:aldahttps://stats.idre.ucla.edu/wp-content/uploads/2016/02/ALDACh5Table5.4.txt"; Variable: Names are id exp1-exp13 lnw1-lnw13 black hgc_9; Missing are all (-999) ; Usevariables are exp1-exp13 lnw1-lnw13; Tscores exp1-exp13; Analysis: Type = random; estimator = ml; MODEL: i s | lnw1-lnw13 at exp1-exp13; i with s; lnw1-lnw13 (1) ;
-----------------------------------------------------------------------------
MODEL FIT INFORMATION
Number of Free Parameters 6
Loglikelihood
H0 Value -2460.697
Information Criteria
Akaike (AIC) 4933.394
Bayesian (BIC) 4962.128
Sample-Size Adjusted BIC 4943.073
(n* = (n + 2) / 24)
MODEL RESULTS
Two-Tailed
Estimate S.E. Est./S.E. P-Value
I WITH
S -0.003 0.001 -3.354 0.001
Means
I 1.716 0.011 158.792 0.000
S 0.046 0.002 19.461 0.000
Intercepts
LNW1 0.000 0.000 999.000 999.000
LNW2 0.000 0.000 999.000 999.000
LNW3 0.000 0.000 999.000 999.000
LNW4 0.000 0.000 999.000 999.000
LNW5 0.000 0.000 999.000 999.000
LNW6 0.000 0.000 999.000 999.000
LNW7 0.000 0.000 999.000 999.000
LNW8 0.000 0.000 999.000 999.000
LNW9 0.000 0.000 999.000 999.000
LNW10 0.000 0.000 999.000 999.000
LNW11 0.000 0.000 999.000 999.000
LNW12 0.000 0.000 999.000 999.000
LNW13 0.000 0.000 999.000 999.000
Variances
I 0.054 0.005 10.851 0.000
S 0.002 0.000 7.845 0.000
Residual Variances
LNW1 0.095 0.002 48.916 0.000
LNW2 0.095 0.002 48.916 0.000
LNW3 0.095 0.002 48.916 0.000
LNW4 0.095 0.002 48.916 0.000
LNW5 0.095 0.002 48.916 0.000
LNW6 0.095 0.002 48.916 0.000
LNW7 0.095 0.002 48.916 0.000
LNW8 0.095 0.002 48.916 0.000
LNW9 0.095 0.002 48.916 0.000
LNW10 0.095 0.002 48.916 0.000
LNW11 0.095 0.002 48.916 0.000
LNW12 0.095 0.002 48.916 0.000
LNW13 0.095 0.002 48.916 0.000
Model B
Title:
Table 5.4, Model B, Person (wide) file
Data:
File is "E:sandwmplushttps://stats.idre.ucla.edu/wp-content/uploads/2016/02/ALDACh5Table5.4.txt";
Variable:
Names are
id exp1-exp13 lnw1-lnw13 black hgc_9;
Missing are all (-999) ;
Usevariables are
exp1-exp13 lnw1-lnw13 black hgc_9;
Tscores exp1-exp13;
Analysis:
Type = random;
estimator = ml;
MODEL:
i s | lnw1-lnw13 at exp1-exp13;
i with s;
i on black hgc_9;
s on black hgc_9;
lnw1-lnw13 (1);
-----------------------------------------------------------------------------
MODEL FIT INFORMATION
Number of Free Parameters 10
Loglikelihood
H0 Value -2436.876
Information Criteria
Akaike (AIC) 4893.751
Bayesian (BIC) 4941.641
Sample-Size Adjusted BIC 4909.883
(n* = (n + 2) / 24)
MODEL RESULTS
Two-Tailed
Estimate S.E. Est./S.E. P-Value
I ON
BLACK 0.015 0.024 0.643 0.520
HGC_9 0.035 0.008 4.430 0.000
S ON
BLACK -0.018 0.005 -3.312 0.001
HGC_9 0.001 0.002 0.742 0.458
I WITH
S -0.003 0.001 -3.378 0.001
Intercepts
LNW1 0.000 0.000 999.000 999.000
LNW2 0.000 0.000 999.000 999.000
LNW3 0.000 0.000 999.000 999.000
LNW4 0.000 0.000 999.000 999.000
LNW5 0.000 0.000 999.000 999.000
LNW6 0.000 0.000 999.000 999.000
LNW7 0.000 0.000 999.000 999.000
LNW8 0.000 0.000 999.000 999.000
LNW9 0.000 0.000 999.000 999.000
LNW10 0.000 0.000 999.000 999.000
LNW11 0.000 0.000 999.000 999.000
LNW12 0.000 0.000 999.000 999.000
LNW13 0.000 0.000 999.000 999.000
I 1.717 0.013 136.842 0.000
S 0.049 0.003 18.722 0.000
Residual Variances
LNW1 0.095 0.002 48.913 0.000
LNW2 0.095 0.002 48.913 0.000
LNW3 0.095 0.002 48.913 0.000
LNW4 0.095 0.002 48.913 0.000
LNW5 0.095 0.002 48.913 0.000
LNW6 0.095 0.002 48.913 0.000
LNW7 0.095 0.002 48.913 0.000
LNW8 0.095 0.002 48.913 0.000
LNW9 0.095 0.002 48.913 0.000
LNW10 0.095 0.002 48.913 0.000
LNW11 0.095 0.002 48.913 0.000
LNW12 0.095 0.002 48.913 0.000
LNW13 0.095 0.002 48.913 0.000
I 0.052 0.005 10.629 0.000
S 0.002 0.000 7.648 0.000
Model C
Title:
Table 5.4, Model A, Person (wide) file
Data:
File is "E:sandwmplushttps://stats.idre.ucla.edu/wp-content/uploads/2016/02/ALDACh5Table5.4.txt";
Variable:
Names are
id exp1-exp13 lnw1-lnw13 black hgc_9;
Missing are all (-999) ;
Usevariables are
exp1-exp13 lnw1-lnw13 black hgc_9;
Tscores exp1-exp13;
Analysis:
Type = random;
estimator = ml;
MODEL:
i s | lnw1-lnw13 at exp1-exp13;
i with s;
i on hgc_9;
s on black;
lnw1-lnw13 (1);
-----------------------------------------------------------------------------
MODEL FIT INFORMATION
Number of Free Parameters 8
Loglikelihood
H0 Value -2437.352
Information Criteria
Akaike (AIC) 4890.704
Bayesian (BIC) 4929.015
Sample-Size Adjusted BIC 4903.609
(n* = (n + 2) / 24)
MODEL RESULTS
Two-Tailed
Estimate S.E. Est./S.E. P-Value
I ON
HGC_9 0.038 0.006 5.961 0.000
S ON
BLACK -0.016 0.005 -3.572 0.000
I WITH
S -0.003 0.001 -3.406 0.001
Intercepts
LNW1 0.000 0.000 999.000 999.000
LNW2 0.000 0.000 999.000 999.000
LNW3 0.000 0.000 999.000 999.000
LNW4 0.000 0.000 999.000 999.000
LNW5 0.000 0.000 999.000 999.000
LNW6 0.000 0.000 999.000 999.000
LNW7 0.000 0.000 999.000 999.000
LNW8 0.000 0.000 999.000 999.000
LNW9 0.000 0.000 999.000 999.000
LNW10 0.000 0.000 999.000 999.000
LNW11 0.000 0.000 999.000 999.000
LNW12 0.000 0.000 999.000 999.000
LNW13 0.000 0.000 999.000 999.000
I 1.721 0.011 160.860 0.000
S 0.049 0.003 19.416 0.000
Residual Variances
LNW1 0.095 0.002 48.925 0.000
LNW2 0.095 0.002 48.925 0.000
LNW3 0.095 0.002 48.925 0.000
LNW4 0.095 0.002 48.925 0.000
LNW5 0.095 0.002 48.925 0.000
LNW6 0.095 0.002 48.925 0.000
LNW7 0.095 0.002 48.925 0.000
LNW8 0.095 0.002 48.925 0.000
LNW9 0.095 0.002 48.925 0.000
LNW10 0.095 0.002 48.925 0.000
LNW11 0.095 0.002 48.925 0.000
LNW12 0.095 0.002 48.925 0.000
LNW13 0.095 0.002 48.925 0.000
I 0.052 0.005 10.636 0.000
S 0.002 0.000 7.691 0.000
Table 5.7, page 163
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:aldaunemp.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:aldaunemp_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:aldaunemp_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 C:aldaunemployment_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.
We thank Hemant Kher for providing the Mplus code for this example.
NOTE: The results obtained from Mplus do not match those shown in the text.
Regarding these differences, Professor Bengt Muthen says:
"Notice that there are some numerical issues with the model – the variance-covariance matrix of the random effects is singular. Just a little more information about Mplus. If you add the technical option output:tech8; you will see the details of the convergence process. The default algorithm EMA quickly reaches the ML estimates but fails because the variance covariance matrix for the random effects is singular. At that point Mplus switches to the EM algorithm which slowly approaches the singularity, but Mplus will deliberately avoid the full convergence to avoid the singularity. In this part of the algorithm the solution is driven by the logcriterion convergence criterion. So essentially all software packages differ because the ML solution is inadmissible, so they report their own version of "approximately" ML solution."
Title:
Table 5.7, Model D, Person Period (long) file
Unemp and Unemp*Months (monBYun) have fixed
as well as random effects
Data:
File is "C:aldaunemployment_pp.dat";
Define:
monBYun = months * unemp;
Variable:
Names are
id months cesd unemp;
Missing are all (-999999999) ;
Usevariables are
cesd unemp monBYun;
cluster = id;
within = unemp monBYun;
Analysis:
Type = random missing twolevel ;
logcriterion=0.0000001; miter=10000;
estimator = ml;
model:
%within%
s1 | cesd on unemp;
s2 | cesd on monBYun;
%between%
cesd with s1;
cesd with s2;
s1 with s2;
output: sampstat;
-----------------------------------------------------------------------------
TESTS OF MODEL FIT
Loglikelihood
H0 Value -2547.651
Information Criteria
Number of Free Parameters 10
Akaike (AIC) 5115.302
Bayesian (BIC) 5160.434
Sample-Size Adjusted BIC 5128.683
(n* = (n + 2) / 24)
MODEL RESULTS
Two-Tailed
Estimate S.E. Est./S.E. P-Value
Within Level
Residual Variances
CESD 59.013 6.030 9.787 0.000
Between Level
CESD WITH
S1 6.539 11.457 0.571 0.568
S2 0.647 2.252 0.287 0.774
S1 WITH
S2 -5.625 2.656 -2.118 0.034
Means
CESD 11.195 0.795 14.080 0.000
S1 6.927 0.933 7.421 0.000
S2 -0.303 0.114 -2.668 0.008
Variances
CESD 45.261 12.558 3.604 0.000
S1 44.973 21.099 2.132 0.033
S2 0.754 0.264 2.859 0.004
Table 5.8, page 175 using https://stats.idre.ucla.edu/wp-content/uploads/2016/02/ALDACh5Table5.8.txt
We thank Hemant Kher for providing the Mplus code for this example.
Model A: Centered at 7.
Title:
Table 5.8, Model A, Person Period (long) file
Data:
File is "C:aldahttps://stats.idre.ucla.edu/wp-content/uploads/2016/02/ALDACh5Table5.8.txt";
Define:
uerate7 = uerate-7;
hgc_9=hgc-9;
Variable:
Names are
id lnw exper black hgc uerate ue_c1 ue_mean ue_p_c ue1;
Usevariables are
lnw exper black uerate7 hgc_9;
cluster = id;
within = exper uerate7 hgc_9;
between = black;
Analysis:
Type = random missing twolevel ;
mconv=0.0000001;
estimator = ml;
model:
%within%
lnw on hgc_9 uerate7;
s | lnw on exper;
%between%
lnw with s;
s on black;
-----------------------------------------------------------------------------
TESTS OF MODEL FIT
Loglikelihood
H0 Value -2415.260
Information Criteria
Number of Free Parameters 9
Akaike (AIC) 4848.519
Bayesian (BIC) 4909.398
Sample-Size Adjusted BIC 4880.799
(n* = (n + 2) / 24)
MODEL RESULTS
Two-Tailed
Estimate S.E. Est./S.E. P-Value
Within Level
LNW ON
HGC_9 0.040 0.006 6.287 0.000
UERATE7 -0.012 0.002 -6.663 0.000
Residual Variances
LNW 0.095 0.002 48.909 0.000
Between Level
S ON
BLACK -0.018 0.004 -4.055 0.000
LNW WITH
S -0.003 0.001 -3.474 0.001
Means
LNW 1.749 0.011 153.413 0.000
Intercepts
S 0.044 0.003 16.907 0.000
Variances
LNW 0.051 0.005 10.531 0.000
Residual Variances
S 0.002 0.000 7.676 0.000
Model B: Within-person centering.
Title:
Table 5.8, Model B, Person Period (long) file
Data:
File is "C:aldahttps://stats.idre.ucla.edu/wp-content/uploads/2016/02/ALDACh5Table5.8.txt";
Variable:
Names are
id lnw exper black hgc uerate ue_c1 ue_mean ue_p_c ue1;
! Note: ue_mean=person's mean uerate
! Note: ue_p_c =uerate centered around the person's mean uerate
Usevariables are
lnw exper black ue_mean ue_p_c hgc_9;
cluster = id;
within = exper ue_mean ue_p_c hgc_9;
between = black;
Define:
hgc_9=hgc-9;
Analysis:
Type = random twolevel ;
mconv=0.0000001;
estimator = ml;
model:
%within%
lnw on hgc_9 ue_mean ue_p_c;
s | lnw on exper;
%between%
lnw with s;
s on black;
output: sampstat;
-----------------------------------------------------------------------------
TESTS OF MODEL FIT
Loglikelihood
H0 Value -2413.489
Information Criteria
Number of Free Parameters 10
Akaike (AIC) 4846.978
Bayesian (BIC) 4914.622
Sample-Size Adjusted BIC 4882.845
(n* = (n + 2) / 24)
MODEL RESULTS
Two-Tailed
Estimate S.E. Est./S.E. P-Value
Within Level
LNW ON
HGC_9 0.040 0.006 6.323 0.000
UE_MEAN -0.018 0.004 -4.999 0.000
UE_P_C -0.010 0.002 -4.719 0.000
Residual Variances
LNW 0.095 0.002 48.907 0.000
Between Level
S ON
BLACK -0.019 0.004 -4.214 0.000
LNW WITH
S -0.003 0.001 -3.588 0.000
Means
LNW 1.874 0.030 63.194 0.000
Intercepts
S 0.045 0.003 16.971 0.000
Variances
LNW 0.051 0.005 10.537 0.000
Residual Variances
S 0.002 0.000 7.673 0.000
Model C: Time-1 centered.
Title:
Table 5.8, Model C, Person Period (long) file
Data:
File is "C:aldahttps://stats.idre.ucla.edu/wp-content/uploads/2016/02/ALDACh5Table5.8.txt";
Variable:
Names are
id lnw exper black hgc uerate ue_c1 ue_mean ue_p_c ue1;
! Note: ue_c1 =uerate centered around the person's 1st value of uerate
! Note: ue1 =the first uerate value for the person
Usevariables are
lnw exper black ue1 ue_c1 hgc_9;
cluster = id;
within = exper ue1 ue_c1 hgc_9;
between = black;
Define:
hgc_9=hgc-9;
Analysis:
Type = random twolevel ;
mconv=0.0000001;
estimator = ml;
Model:
%within%
lnw on hgc_9 ue1 ue_c1;
s | lnw on exper;
%between%
lnw with s;
s on black;
Output: sampstat;
-----------------------------------------------------------------------------
TESTS OF MODEL FIT
Loglikelihood
H0 Value -2412.921
Information Criteria
Number of Free Parameters 10
Akaike (AIC) 4845.842
Bayesian (BIC) 4913.485
Sample-Size Adjusted BIC 4881.708
(n* = (n + 2) / 24)
MODEL RESULTS
Two-Tailed
Estimate S.E. Est./S.E. P-Value
Within Level
LNW ON
HGC_9 0.040 0.006 6.287 0.000
UE1 -0.016 0.003 -6.107 0.000
UE_C1 -0.010 0.002 -5.294 0.000
Residual Variances
LNW 0.095 0.002 48.922 0.000
Between Level
S ON
BLACK -0.018 0.004 -4.086 0.000
LNW WITH
S -0.003 0.001 -3.463 0.001
Means
LNW 1.869 0.026 71.797 0.000
Intercepts
S 0.045 0.003 17.043 0.000
Variances
LNW 0.050 0.005 10.498 0.000
Residual Variances
S 0.002 0.000 7.682 0.000
Table 5.10, page 184 using https://stats.idre.ucla.edu/wp-content/uploads/2016/02/ALDACh5Table5.10.txt
We thank Hemant Kher for providing the Mplus code for this example.
Model A: Time.
Title:
Table 5.10, Model A, Person Period (long) file
Data:
File is "C:ALDAaldach5table5.10.txt";
Variable:
Names are
id treat wave day tofday time
time333 time667 initial final pos;
Usevariables are
pos time treat;
cluster = id;
within = time;
between = treat;
Analysis:
Type = random twolevel ;
mconvergence = .000001;
estimator = ml;
model:
%within%
s | pos on time;
%between%
pos with s;
pos s on treat;
-----------------------------------------------------------------------------
TESTS OF MODEL FIT
Loglikelihood
H0 Value -6340.226
Information Criteria
Number of Free Parameters 8
Akaike (AIC) 12696.452
Bayesian (BIC) 12737.448
Sample-Size Adjusted BIC 12712.036
(n* = (n + 2) / 24)
MODEL RESULTS
Two-Tailed
Estimate S.E. Est./S.E. P-Value
Within Level
Residual Variances
POS 1230.014 52.098 23.609 0.000
Between Level
S ON
TREAT 5.536 2.276 2.432 0.015
POS ON
TREAT -3.109 12.329 -0.252 0.801
POS WITH
S -121.138 58.866 -2.058 0.040
Intercepts
POS 167.462 9.323 17.962 0.000
S -2.417 1.730 -1.398 0.162
Residual Variances
POS 2109.669 419.601 5.028 0.000
S 63.600 14.224 4.471 0.000
Model B: Time – 3.33.
Title:
Table 5.10, Model B, Person Period (long) file
Data:
File is "C:ALDAaldach5table5.10.txt";
Variable:
Names are
id treat wave day tofday time
time333 time667 initial final pos;
Usevariables are
pos time333 treat;
cluster = id;
within = time333;
between = treat;
Analysis:
Type = random twolevel ;
mconvergence = .000001;
estimator = ml;
model:
%within%
s | pos on time333;
%between%
pos with s;
pos s on treat;
-----------------------------------------------------------------------------
TESTS OF MODEL FIT
Loglikelihood
H0 Value -6340.226
Information Criteria
Number of Free Parameters 8
Akaike (AIC) 12696.452
Bayesian (BIC) 12737.447
Sample-Size Adjusted BIC 12712.036
(n* = (n + 2) / 24)
MODEL RESULTS
Two-Tailed
Estimate S.E. Est./S.E. P-Value
Within Level
Residual Variances
POS 1230.008 52.098 23.610 0.000
Between Level
S ON
TREAT 5.536 2.277 2.432 0.015
POS ON
TREAT 15.346 11.545 1.329 0.184
POS WITH
S 90.845 52.378 1.734 0.083
Intercepts
POS 159.404 8.765 18.187 0.000
S -2.416 1.730 -1.397 0.162
Residual Variances
POS 2008.740 367.251 5.470 0.000
S 63.604 14.225 4.471 0.000
Model C: Time – 6.67.
Title:
Table 5.10, Model C, Person Period (long) file
Data:
File is "C:ALDAaldach5table5.10.txt";
Variable:
Names are
id treat wave day tofday time
time333 time667 initial final pos;
Usevariables are
pos time667 treat;
cluster = id;
within = time667;
between = treat;
Analysis:
Type = random twolevel ;
mconvergence = .000001;
estimator = ml;
Model:
%within%
s | pos on time667;
%between%
pos with s;
pos s on treat;
-----------------------------------------------------------------------------
TESTS OF MODEL FIT
Loglikelihood
H0 Value -6340.226
Information Criteria
Number of Free Parameters 8
Akaike (AIC) 12696.452
Bayesian (BIC) 12737.448
Sample-Size Adjusted BIC 12712.036
(n* = (n + 2) / 24)
MODEL RESULTS
Two-Tailed
Estimate S.E. Est./S.E. P-Value
Within Level
Residual Variances
POS 1230.016 52.099 23.609 0.000
Between Level
S ON
TREAT 5.535 2.276 2.432 0.015
POS ON
TREAT 33.797 15.156 2.230 0.026
POS WITH
S 302.802 80.708 3.752 0.000
Intercepts
POS 151.349 11.541 13.114 0.000
S -2.417 1.730 -1.397 0.162
Residual Variances
POS 3320.892 631.560 5.258 0.000
S 63.588 14.219 4.472 0.000