Mplus VERSION 6
MUTHEN & MUTHEN
04/25/2010 10:57 PM
INPUT INSTRUCTIONS
! SCRIPT NAME : ctVCut2d (cb)
! GOAL : To evaluate best model for variance components A D E
! DATA : ordinal
! INPUT : contingency tables
! UNI/BI/MULTI : uni
! DATA-GROUPS : MZ DZ
! MEANS MODEL : -
! VARIANCE COVARIANCE MODEL(S) :
! 1. ADE
! 2. AE
! 3. E
data: file is ct.dat;
variable: names are g y1 y2 weight;
categorical=y1 y2;
grouping=g(1=MZ 2=DZ); ! specify the two groups MZ and DZ
freqweight=weight;
model:
[y1$1 y2$1] (t);
y1 with y2 (mzc);
model dz:
y1 with y2 (dzc);
model constraint:
! Uncomment for Model ADE
new(a d e x w z);
a=x*x;
d=w*w;
e=1-x*x-w*w;
z=sqrt(1-x*x-w*w);
mzc=x*x+w*w;
dzc=0.5*x*x+0.25*w*w;
! Uncomment for Model AE
! d=0;
! Uncomment for Model DE
! a=0;
! Uncomment for Model E
! a=0; d=0;
INPUT READING TERMINATED NORMALLY
SUMMARY OF ANALYSIS
Number of groups 2
Number of observations
Group MZ 702
Group DZ 726
Number of patterns
Group MZ 4
Group DZ 4
Number of dependent variables 2
Number of independent variables 0
Number of continuous latent variables 0
Observed dependent variables
Binary and ordered categorical (ordinal)
Y1 Y2
Variables with special functions
Grouping variable G
Weight variable WEIGHT
Estimator WLSMV
Maximum number of iterations 1000
Convergence criterion 0.500D-04
Maximum number of steepest descent iterations 20
Parameterization DELTA
Input data file(s)
ct.dat
Input data format FREE
UNIVARIATE PROPORTIONS AND COUNTS FOR CATEGORICAL VARIABLES
Group MZ
Y1
Category 1 0.744 522.000
Category 2 0.256 180.000
Y2
Category 1 0.761 534.000
Category 2 0.239 168.000
Group DZ
Y1
Category 1 0.753 547.000
Category 2 0.247 179.000
Y2
Category 1 0.773 561.000
Category 2 0.227 165.000
THE MODEL ESTIMATION TERMINATED NORMALLY
TESTS OF MODEL FIT
Chi-Square Test of Model Fit
Value 1.888*
Degrees of Freedom 3
P-Value 0.5961
Chi-Square Contributions From Each Group
MZ 0.840
DZ 1.048
* The chi-square value for MLM, MLMV, MLR, ULSMV, WLSM and WLSMV cannot be used
for chi-square difference testing in the regular way. MLM, MLR and WLSM
chi-square difference testing is described on the Mplus website. MLMV, WLSMV,
and ULSMV difference testing is done using the DIFFTEST option.
Chi-Square Test of Model Fit for the Baseline Model
Value 56.109
Degrees of Freedom 2
P-Value 0.0000
CFI/TLI
CFI 1.000
TLI 1.014
Number of Free Parameters 3
RMSEA (Root Mean Square Error Of Approximation)
Estimate 0.000
WRMR (Weighted Root Mean Square Residual)
Value 0.766
MODEL RESULTS
Two-Tailed
Estimate S.E. Est./S.E. P-Value
Group MZ
Y1 WITH
Y2 0.420 0.060 7.057 0.000
Thresholds
Y1$1 0.699 0.028 25.147 0.000
Y2$1 0.699 0.028 25.147 0.000
Group DZ
Y1 WITH
Y2 0.171 0.068 2.512 0.012
Thresholds
Y1$1 0.699 0.028 25.147 0.000
Y2$1 0.699 0.028 25.147 0.000
New/Additional Parameters
A 0.262 0.278 0.942 0.346
D 0.158 0.297 0.534 0.594
E 0.580 0.060 9.731 0.000
X 0.512 0.272 1.884 0.060
W 0.398 0.373 1.067 0.286
Z 0.761 0.039 19.462 0.000
QUALITY OF NUMERICAL RESULTS
Condition Number for the Information Matrix 0.312E-02
(ratio of smallest to largest eigenvalue)
Beginning Time: 22:57:38
Ending Time: 22:57:38
Elapsed Time: 00:00:00
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