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

INPUT INSTRUCTIONS

  TITLE:	this is an example of a multiple indicator
  	linear growth model for continuous outcomes
  DATA:	FILE IS ex6.14.dat;
  VARIABLE:	NAMES ARE y11 y21 y31 y12 y22 y32 y13
  	y23 y33;
  MODEL:	f1 BY	y11
                  	y21-y31 (1-2);
  	f2 BY 	y12
  			y22-y32 (1-2);
  	f3 BY 	y13
  			y23-y33 (1-2);
  	[y11 y12 y13] (3);
  	[y21 y22 y23] (4);
  	[y31 y32 y33] (5);
  	i s | f1@0 f2@1 f3@2;




INPUT READING TERMINATED NORMALLY



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

  Continuous
   Y11         Y21         Y31         Y12         Y22         Y32
   Y13         Y23         Y33

Continuous latent variables
   F1          F2          F3          I           S


Estimator                                                       ML
Information matrix                                        OBSERVED
Maximum number of iterations                                  1000
Convergence criterion                                    0.500D-04
Maximum number of steepest descent iterations                   20

Input data file(s)
  ex6.14.dat

Input data format  FREE



THE MODEL ESTIMATION TERMINATED NORMALLY



MODEL FIT INFORMATION

Number of Free Parameters                       21

Loglikelihood

          H0 Value                       -6357.999
          H1 Value                       -6339.524

Information Criteria

          Akaike (AIC)                   12757.998
          Bayesian (BIC)                 12846.504
          Sample-Size Adjusted BIC       12779.849
            (n* = (n + 2) / 24)

Chi-Square Test of Model Fit

          Value                             36.949
          Degrees of Freedom                    33
          P-Value                           0.2914

RMSEA (Root Mean Square Error Of Approximation)

          Estimate                           0.015
          90 Percent C.I.                    0.000  0.037
          Probability RMSEA <= .05           0.998

CFI/TLI

          CFI                                0.999
          TLI                                0.999

Chi-Square Test of Model Fit for the Baseline Model

          Value                           3565.847
          Degrees of Freedom                    36
          P-Value                           0.0000

SRMR (Standardized Root Mean Square Residual)

          Value                              0.023



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
    Y11                1.000      0.000    999.000    999.000
    Y21                1.003      0.018     56.519      0.000
    Y31                0.960      0.018     53.259      0.000

 F2       BY
    Y12                1.000      0.000    999.000    999.000
    Y22                1.003      0.018     56.519      0.000
    Y32                0.960      0.018     53.259      0.000

 F3       BY
    Y13                1.000      0.000    999.000    999.000
    Y23                1.003      0.018     56.519      0.000
    Y33                0.960      0.018     53.259      0.000

 S        WITH
    I                 -0.102      0.066     -1.547      0.122

 Means
    I                  0.000      0.000    999.000    999.000
    S                  1.023      0.033     30.768      0.000

 Intercepts
    Y11               -0.519      0.059     -8.816      0.000
    Y21               -0.017      0.059     -0.296      0.767
    Y31                0.540      0.057      9.500      0.000
    Y12               -0.519      0.059     -8.816      0.000
    Y22               -0.017      0.059     -0.296      0.767
    Y32                0.540      0.057      9.500      0.000
    Y13               -0.519      0.059     -8.816      0.000
    Y23               -0.017      0.059     -0.296      0.767
    Y33                0.540      0.057      9.500      0.000
    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

 Variances
    I                  1.125      0.129      8.714      0.000
    S                  0.269      0.060      4.470      0.000

 Residual Variances
    Y11                0.519      0.047     10.981      0.000
    Y21                0.466      0.045     10.400      0.000
    Y31                0.543      0.047     11.620      0.000
    Y12                0.520      0.046     11.322      0.000
    Y22                0.510      0.046     11.168      0.000
    Y32                0.497      0.044     11.367      0.000
    Y13                0.542      0.048     11.243      0.000
    Y23                0.378      0.040      9.557      0.000
    Y33                0.536      0.046     11.725      0.000
    F1                 0.329      0.111      2.962      0.003
    F2                 0.501      0.066      7.559      0.000
    F3                 0.210      0.126      1.664      0.096


QUALITY OF NUMERICAL RESULTS

     Condition Number for the Information Matrix              0.237E-02
       (ratio of smallest to largest eigenvalue)


     Beginning Time:  17:48:46
        Ending Time:  17:48:46
       Elapsed Time:  00:00:00



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