Mplus VERSION 7.3
MUTHEN & MUTHEN
09/22/2014   5:48 PM

INPUT INSTRUCTIONS

  TITLE:	this is an example of a CFA with
  	continuous and categorical factor
  	indicators
  DATA:	FILE IS ex5.3.dat;
  VARIABLE:	NAMES ARE u1-u3 y4-y6;
  	CATEGORICAL ARE u1 u2 u3;
  MODEL:	f1 BY u1-u3;
  	f2 BY y4-y6;



INPUT READING TERMINATED NORMALLY



this is an example of a CFA with
continuous and 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

  Continuous
   Y4          Y5          Y6

  Binary and ordered categorical (ordinal)
   U1          U2          U3

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)
  ex5.3.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



THE MODEL ESTIMATION TERMINATED NORMALLY



MODEL FIT INFORMATION

Number of Free Parameters                       16

Chi-Square Test of Model Fit

          Value                              3.935*
          Degrees of Freedom                     8
          P-Value                           0.8629

*   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  0.028
          Probability RMSEA <= .05           0.995

CFI/TLI

          CFI                                1.000
          TLI                                1.005

Chi-Square Test of Model Fit for the Baseline Model

          Value                           1699.445
          Degrees of Freedom                    15
          P-Value                           0.0000

WRMR (Weighted Root Mean Square Residual)

          Value                              0.199



MODEL RESULTS

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

 F1       BY
    U1                 1.000      0.000    999.000    999.000
    U2                 1.066      0.043     24.666      0.000
    U3                 1.000      0.039     25.935      0.000

 F2       BY
    Y4                 1.000      0.000    999.000    999.000
    Y5                 1.051      0.039     26.737      0.000
    Y6                 0.997      0.041     24.331      0.000

 F2       WITH
    F1                -0.004      0.044     -0.094      0.925

 Intercepts
    Y4                -0.016      0.045     -0.350      0.726
    Y5                -0.016      0.045     -0.358      0.720
    Y6                 0.014      0.044      0.312      0.755

 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

 Variances
    F1                 0.801      0.046     17.468      0.000
    F2                 0.774      0.062     12.504      0.000

 Residual Variances
    Y4                 0.251      0.025     10.219      0.000
    Y5                 0.170      0.021      7.950      0.000
    Y6                 0.218      0.021     10.507      0.000


R-SQUARE

    Observed                   Residual
    Variable        Estimate   Variance

    U1                 0.801      0.199
    U2                 0.911      0.089
    U3                 0.802      0.198
    Y4                 0.755
    Y5                 0.834
    Y6                 0.779


QUALITY OF NUMERICAL RESULTS

     Condition Number for the Information Matrix              0.393E-02
       (ratio of smallest to largest eigenvalue)


     Beginning Time:  17:48:38
        Ending Time:  17:48:38
       Elapsed Time:  00:00:00



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