Mplus VERSION 7
MUTHEN & MUTHEN
09/22/2012  10:55 PM

INPUT INSTRUCTIONS

  TITLE:	this is an example of a CFA with
  	categorical factor indicators
  DATA:	FILE IS ex5.2.dat;
  VARIABLE:	NAMES ARE u1-u6;
  	CATEGORICAL ARE u1-u6;
  MODEL:	f1 BY u1-u3;
  	f2 BY u4-u6;



INPUT READING TERMINATED NORMALLY



this is an example of a CFA with
categorical factor indicators

SUMMARY OF ANALYSIS

Number of groups                                                 1
Number of observations                                         500

Number of dependent variables                                    6
Number of independent variables                                  0
Number of continuous latent variables                            2

Observed dependent variables

  Binary and ordered categorical (ordinal)
   U1          U2          U3          U4          U5          U6

Continuous latent variables
   F1          F2


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)
  ex5.2.dat

Input data format  FREE


UNIVARIATE PROPORTIONS AND COUNTS FOR CATEGORICAL VARIABLES

    U1
      Category 1    0.504      252.000
      Category 2    0.496      248.000
    U2
      Category 1    0.506      253.000
      Category 2    0.494      247.000
    U3
      Category 1    0.496      248.000
      Category 2    0.504      252.000
    U4
      Category 1    0.524      262.000
      Category 2    0.476      238.000
    U5
      Category 1    0.508      254.000
      Category 2    0.492      246.000
    U6
      Category 1    0.510      255.000
      Category 2    0.490      245.000



THE MODEL ESTIMATION TERMINATED NORMALLY



MODEL FIT INFORMATION

Number of Free Parameters                       13

Chi-Square Test of Model Fit

          Value                              5.482*
          Degrees of Freedom                     8
          P-Value                           0.7051

*   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  0.040
          Probability RMSEA <= .05           0.983

CFI/TLI

          CFI                                1.000
          TLI                                1.002

Chi-Square Test of Model Fit for the Baseline Model

          Value                           2808.627
          Degrees of Freedom                    15
          P-Value                           0.0000

WRMR (Weighted Root Mean Square Residual)

          Value                              0.342



MODEL RESULTS

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

 F1       BY
    U1                 1.000      0.000    999.000    999.000
    U2                 1.067      0.043     24.661      0.000
    U3                 1.001      0.039     25.913      0.000

 F2       BY
    U4                 1.000      0.000    999.000    999.000
    U5                 1.109      0.054     20.482      0.000
    U6                 1.030      0.047     21.988      0.000

 F2       WITH
    F1                -0.021      0.049     -0.432      0.666

 Thresholds
    U1$1               0.010      0.056      0.179      0.858
    U2$1               0.015      0.056      0.268      0.788
    U3$1              -0.010      0.056     -0.179      0.858
    U4$1               0.060      0.056      1.073      0.283
    U5$1               0.020      0.056      0.358      0.721
    U6$1               0.025      0.056      0.447      0.655

 Variances
    F1                 0.800      0.046     17.439      0.000
    F2                 0.736      0.052     14.099      0.000


R-SQUARE

    Observed                   Residual
    Variable        Estimate   Variance

    U1                 0.800      0.200
    U2                 0.912      0.088
    U3                 0.802      0.198
    U4                 0.736      0.264
    U5                 0.906      0.094
    U6                 0.781      0.219


QUALITY OF NUMERICAL RESULTS

     Condition Number for the Information Matrix              0.200E-01
       (ratio of smallest to largest eigenvalue)


     Beginning Time:  22:55:30
        Ending Time:  22:55:30
       Elapsed Time:  00:00:00



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