Mplus VERSION 7.2
MUTHEN & MUTHEN
05/07/2014   2:40 PM

INPUT INSTRUCTIONS

  TITLE:	this is an example of a multiple indicator
  	linear growth model for categorical outcomes
  DATA:	FILE IS ex6.15.dat;
  VARIABLE:	NAMES ARE u11 u21 u31 u12 u22 u32
  	u13 u23 u33;
  	CATEGORICAL ARE u11 u21 u31 u12 u22 u32 u13 u23 u33;

  MODEL:	f1 BY	u11
                  	u21-u31 (1-2);
  	f2 BY 	u12
  			u22-u32 (1-2);
  	f3 BY	u13
  			u23-u33 (1-2);
  	[u11$1 u12$1 u13$1] (3);
  	[u21$1 u22$1 u23$1] (4);
  	[u31$1 u32$1 u33$1] (5);
  	{u11-u31@1 u12-u33};
  	i s | f1@0 f2@1 f3@2;



INPUT READING TERMINATED NORMALLY



this is an example of a multiple indicator
linear growth model for categorical outcomes

SUMMARY OF ANALYSIS

Number of groups                                                 1
Number of observations                                         500

Number of dependent variables                                    9
Number of independent variables                                  0
Number of continuous latent variables                            5

Observed dependent variables

  Binary and ordered categorical (ordinal)
   U11         U21         U31         U12         U22         U32
   U13         U23         U33

Continuous latent variables
   F1          F2          F3          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)
  ex6.15.dat

Input data format  FREE


UNIVARIATE PROPORTIONS AND COUNTS FOR CATEGORICAL VARIABLES

    U11
      Category 1    0.294      147.000
      Category 2    0.706      353.000
    U21
      Category 1    0.500      250.000
      Category 2    0.500      250.000
    U31
      Category 1    0.684      342.000
      Category 2    0.316      158.000
    U12
      Category 1    0.072       36.000
      Category 2    0.928      464.000
    U22
      Category 1    0.152       76.000
      Category 2    0.848      424.000
    U32
      Category 1    0.298      149.000
      Category 2    0.702      351.000
    U13
      Category 1    0.004        2.000
      Category 2    0.996      498.000
    U23
      Category 1    0.024       12.000
      Category 2    0.976      488.000
    U33
      Category 1    0.062       31.000
      Category 2    0.938      469.000


     WARNING:  THE BIVARIATE TABLE OF U13 AND U11 HAS AN EMPTY CELL.

     WARNING:  THE BIVARIATE TABLE OF U13 AND U21 HAS AN EMPTY CELL.

     WARNING:  THE BIVARIATE TABLE OF U13 AND U31 HAS AN EMPTY CELL.

     WARNING:  THE BIVARIATE TABLE OF U13 AND U22 HAS AN EMPTY CELL.

     WARNING:  THE BIVARIATE TABLE OF U13 AND U32 HAS AN EMPTY CELL.

     WARNING:  THE BIVARIATE TABLE OF U23 AND U13 HAS AN EMPTY CELL.

     WARNING:  THE BIVARIATE TABLE OF U33 AND U13 HAS AN EMPTY CELL.


THE MODEL ESTIMATION TERMINATED NORMALLY



MODEL FIT INFORMATION

Number of Free Parameters                       18

Chi-Square Test of Model Fit

          Value                             25.728*
          Degrees of Freedom                    27
          P-Value                           0.5337

*   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  0.033
          Probability RMSEA <= .05           0.999

CFI/TLI

          CFI                                1.000
          TLI                                1.001

Chi-Square Test of Model Fit for the Baseline Model

          Value                           1559.965
          Degrees of Freedom                    36
          P-Value                           0.0000

WRMR (Weighted Root Mean Square Residual)

          Value                              0.605



MODEL RESULTS

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

 I        |
    F1                 1.000      0.000    999.000    999.000
    F2                 1.000      0.000    999.000    999.000
    F3                 1.000      0.000    999.000    999.000

 S        |
    F1                 0.000      0.000    999.000    999.000
    F2                 1.000      0.000    999.000    999.000
    F3                 2.000      0.000    999.000    999.000

 F1       BY
    U11                1.000      0.000    999.000    999.000
    U21                1.110      0.067     16.651      0.000
    U31                1.002      0.052     19.200      0.000

 F2       BY
    U12                1.000      0.000    999.000    999.000
    U22                1.110      0.067     16.651      0.000
    U32                1.002      0.052     19.200      0.000

 F3       BY
    U13                1.000      0.000    999.000    999.000
    U23                1.110      0.067     16.651      0.000
    U33                1.002      0.052     19.200      0.000

 S        WITH
    I                 -0.103      0.101     -1.020      0.308

 Means
    I                  0.000      0.000    999.000    999.000
    S                  1.062      0.154      6.914      0.000

 Intercepts
    F1                 0.000      0.000    999.000    999.000
    F2                 0.000      0.000    999.000    999.000
    F3                 0.000      0.000    999.000    999.000

 Thresholds
    U11$1             -0.538      0.060     -9.008      0.000
    U21$1             -0.008      0.055     -0.138      0.890
    U31$1              0.483      0.059      8.256      0.000
    U12$1             -0.538      0.060     -9.008      0.000
    U22$1             -0.008      0.055     -0.138      0.890
    U32$1              0.483      0.059      8.256      0.000
    U13$1             -0.538      0.060     -9.008      0.000
    U23$1             -0.008      0.055     -0.138      0.890
    U33$1              0.483      0.059      8.256      0.000

 Variances
    I                  0.560      0.152      3.677      0.000
    S                  0.142      0.092      1.536      0.125

 Residual Variances
    F1                 0.185      0.152      1.220      0.223
    F2                 0.430      0.166      2.597      0.009
    F3                 0.149      0.144      1.035      0.301

 Scales
    U11                1.000      0.000    999.000    999.000
    U21                1.000      0.000    999.000    999.000
    U31                1.000      0.000    999.000    999.000
    U12                0.928      0.114      8.113      0.000
    U22                0.841      0.131      6.419      0.000
    U32                0.930      0.153      6.072      0.000
    U13                0.954      0.125      7.657      0.000
    U23                0.858      0.143      6.001      0.000
    U33                0.928      0.167      5.553      0.000


R-SQUARE

    Observed                   Residual
    Variable        Estimate   Variance

    U11                0.745      0.255
    U21                0.918      0.082
    U31                0.748      0.252
    U12                0.799      0.234
    U22                0.808      0.272
    U32                0.805      0.225
    U13                0.789      0.232
    U23                0.786      0.291
    U33                0.749      0.291

     Latent
    Variable        Estimate

    F1                 0.752
    F2                 0.536
    F3                 0.828


QUALITY OF NUMERICAL RESULTS

     Condition Number for the Information Matrix              0.237E-03
       (ratio of smallest to largest eigenvalue)


     Beginning Time:  14:40:53
        Ending Time:  14:40:53
       Elapsed Time:  00:00:00



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