Mplus VERSION 6
MUTHEN & MUTHEN
04/25/2010 10:57 PM
INPUT INSTRUCTIONS
! SCRIPT NAME : ctSATut2 (cb)
! GOAL : To calculate tetrachoric correlations
! DATA : ordinal
! INPUT : contingency tables
! UNI/BI/MULTI : uni
! DATA-GROUPS : MZ DZ
! MEANS MODEL : -
! VARIANCE COVARIANCE MODEL(S) :
! 1. no model
! 2. as 1, plus thresholds same for twin 1 and twin 2
! 3. as 2, plus thresholds same for MZ and DZ
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] (mzt1);
[y2$1] (mzt2);
y1 with y2 (mzc);
model dz:
[y1$1] (dzt1);
[y2$1] (dzt2);
y1 with y2 (dzc);
model test:
! Uncomment to test equal thresholds for for twin 1 and twin 2
mzt1=mzt2;
dzt1=dzt2;
! Uncomment to test equal thresholds for MZ and DZ
! mzt1=dzt1;
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 0.000*
Degrees of Freedom 0
P-Value 0.0000
Chi-Square Contributions From Each Group
MZ 0.000
DZ 0.000
* 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 1.000
Wald Test of Parameter Constraints
Value 1.559
Degrees of Freedom 2
P-Value 0.4586
Number of Free Parameters 6
RMSEA (Root Mean Square Error Of Approximation)
Estimate 0.000
WRMR (Weighted Root Mean Square Residual)
Value 0.002
MODEL RESULTS
Two-Tailed
Estimate S.E. Est./S.E. P-Value
Group MZ
Y1 WITH
Y2 0.420 0.060 7.058 0.000
Thresholds
Y1$1 0.654 0.051 12.788 0.000
Y2$1 0.709 0.052 13.656 0.000
Group DZ
Y1 WITH
Y2 0.170 0.068 2.508 0.012
Thresholds
Y1$1 0.685 0.051 13.515 0.000
Y2$1 0.748 0.052 14.503 0.000
QUALITY OF NUMERICAL RESULTS
Condition Number for the Information Matrix 0.558E+00
(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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