Mplus VERSION 7.2
MUTHEN & MUTHEN
05/07/2014   2:41 PM

INPUT INSTRUCTIONS

  TITLE:	this is an example of a linear growth
  	model for a categorical outcome
  DATA:	FILE IS ex6.4.dat;
  VARIABLE:	NAMES ARE u11-u14;
  	CATEGORICAL ARE u11-u14;
  MODEL:	i s | u11@0 u12@1 u13@2 u14@3;



INPUT READING TERMINATED NORMALLY



this is an example of a linear growth
model for a categorical outcome

SUMMARY OF ANALYSIS

Number of groups                                                 1
Number of observations                                         500

Number of dependent variables                                    4
Number of independent variables                                  0
Number of continuous latent variables                            2

Observed dependent variables

  Binary and ordered categorical (ordinal)
   U11         U12         U13         U14

Continuous latent variables
   I           S


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)
  ex6.4.dat

Input data format  FREE


UNIVARIATE PROPORTIONS AND COUNTS FOR CATEGORICAL VARIABLES

    U11
      Category 1    0.310      155.000
      Category 2    0.690      345.000
    U12
      Category 1    0.466      233.000
      Category 2    0.534      267.000
    U13
      Category 1    0.624      312.000
      Category 2    0.376      188.000
    U14
      Category 1    0.714      357.000
      Category 2    0.286      143.000



THE MODEL ESTIMATION TERMINATED NORMALLY



MODEL FIT INFORMATION

Number of Free Parameters                        8

Chi-Square Test of Model Fit

          Value                              1.214*
          Degrees of Freedom                     2
          P-Value                           0.5449

*   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.

RMSEA (Root Mean Square Error Of Approximation)

          Estimate                           0.000
          90 Percent C.I.                    0.000  0.077
          Probability RMSEA <= .05           0.819

CFI/TLI

          CFI                                1.000
          TLI                                1.008

Chi-Square Test of Model Fit for the Baseline Model

          Value                            296.848
          Degrees of Freedom                     6
          P-Value                           0.0000

WRMR (Weighted Root Mean Square Residual)

          Value                              0.281



MODEL RESULTS

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

 I        |
    U11                1.000      0.000    999.000    999.000
    U12                1.000      0.000    999.000    999.000
    U13                1.000      0.000    999.000    999.000
    U14                1.000      0.000    999.000    999.000

 S        |
    U11                0.000      0.000    999.000    999.000
    U12                1.000      0.000    999.000    999.000
    U13                2.000      0.000    999.000    999.000
    U14                3.000      0.000    999.000    999.000

 S        WITH
    I                 -0.007      0.054     -0.132      0.895

 Means
    I                  0.000      0.000    999.000    999.000
    S                 -0.407      0.053     -7.680      0.000

 Thresholds
    U11$1             -0.494      0.058     -8.503      0.000
    U12$1             -0.494      0.058     -8.503      0.000
    U13$1             -0.494      0.058     -8.503      0.000
    U14$1             -0.494      0.058     -8.503      0.000

 Variances
    I                  0.439      0.114      3.845      0.000
    S                  0.042      0.035      1.202      0.229

 Scales
    U11                1.000      0.000    999.000    999.000
    U12                1.060      0.149      7.133      0.000
    U13                1.012      0.192      5.259      0.000
    U14                0.772      0.153      5.029      0.000


R-SQUARE

    Observed                   Residual
    Variable        Estimate   Variance

    U11                0.439      0.561
    U12                0.524      0.424
    U13                0.592      0.399
    U14                0.461      0.904


QUALITY OF NUMERICAL RESULTS

     Condition Number for the Information Matrix              0.496E-03
       (ratio of smallest to largest eigenvalue)


     Beginning Time:  14:41:32
        Ending Time:  14:41:32
       Elapsed Time:  00:00:00



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