Mplus VERSION 6
MUTHEN & MUTHEN
04/25/2010  10:57 PM

INPUT INSTRUCTIONS

  ! SCRIPT NAME        : ordSATut2 (cvb)
  ! GOAL                : calculation of tetrachoric correlations, check assumptions of twin
  ! DATA                : ordinal
  ! INPUT                : raw data
  ! UNI/BI/MULTI        : uni
  ! DATA-GROUPS        : MZ DZ
  ! MEANS MODEL        : n.a.
  ! VARIANCE COVARIANCE MODEL(S)        : n.a.
  ! evaluated models:
  ! 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 ordraw1.dat;

  variable: names are id y1 y2 zygot age;
            categorical=y1 y2;
            usevar are y1 y2 g;
            grouping=g(1=MZ 2=DZ);  ! specify the two groups MZ and DZ
            missing=all(-9); ! specify missing data symbol

  define: if (zygot==1 .or. zygot==3) then g=1 else g=2; ! defines the two groups

  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                                                   1290
   Group DZ                                                   1561

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

Estimator                                                    WLSMV
Maximum number of iterations                                  1000
Convergence criterion                                    0.500D-04
Maximum number of steepest descent iterations                   20
Maximum number of iterations for H1                           2000
Convergence criterion for H1                             0.100D-03
Parameterization                                             DELTA

Input data file(s)
  ordraw1.dat

Input data format  FREE


SUMMARY OF DATA

   Group MZ
     Number of missing data patterns             3

   Group DZ
     Number of missing data patterns             3


COVARIANCE COVERAGE OF DATA

Minimum covariance coverage value   0.100


     PROPORTION OF DATA PRESENT FOR MZ


           Covariance Coverage
              Y1            Y2
              ________      ________
 Y1             0.847
 Y2             0.667         0.820


     PROPORTION OF DATA PRESENT FOR DZ


           Covariance Coverage
              Y1            Y2
              ________      ________
 Y1             0.756
 Y2             0.491         0.735


UNIVARIATE PROPORTIONS AND COUNTS FOR CATEGORICAL VARIABLES

  Group MZ
    Y1
      Category 1    0.855      935.000
      Category 2    0.145      158.000
    Y2
      Category 1    0.854      904.000
      Category 2    0.146      154.000

  Group DZ
    Y1
      Category 1    0.875     1033.000
      Category 2    0.125      147.000
    Y2
      Category 1    0.852      977.000
      Category 2    0.148      170.000



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                            202.081
          Degrees of Freedom                     2
          P-Value                           0.0000

CFI/TLI

          CFI                                1.000
          TLI                                1.000

Wald Test of Parameter Constraints

          Value                              3.029
          Degrees of Freedom                     2
          P-Value                           0.2199

Number of Free Parameters                        6

RMSEA (Root Mean Square Error Of Approximation)

          Estimate                           0.000

WRMR (Weighted Root Mean Square Residual)

          Value                              0.001



MODEL RESULTS

                                                    Two-Tailed
                    Estimate       S.E.  Est./S.E.    P-Value

Group MZ

 Y1       WITH
    Y2                 0.667      0.049     13.707      0.000

 Thresholds
    Y1$1               1.060      0.047     22.668      0.000
    Y2$1               1.056      0.047     22.250      0.000

Group DZ

 Y1       WITH
    Y2                 0.308      0.082      3.766      0.000

 Thresholds
    Y1$1               1.152      0.047     24.618      0.000
    Y2$1               1.044      0.045     23.020      0.000


QUALITY OF NUMERICAL RESULTS

     Condition Number for the Information Matrix              0.308E+00
       (ratio of smallest to largest eigenvalue)


     Beginning Time:  22:57:39
        Ending Time:  22:57:39
       Elapsed Time:  00:00:00



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