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

INPUT INSTRUCTIONS

  TITLE: 	this is an example of a growth model for
  	two parallel processes for continuous
  	outcomes with regressions among the random
  	effects
  DATA:	FILE IS ex6.13.dat;
  VARIABLE:	NAMES ARE y11 y12 y13 y14 y21 y22 y23 y24;
  MODEL:	i1 s1 | y11@0 y12@1 y13@2 y14@3;
  	i2 s2 | y21@0 y22@1 y23@2 y24@3;
  	s1 ON i2;
  	s2 ON i1;



INPUT READING TERMINATED NORMALLY



this is an example of a growth model for
two parallel processes for continuous
outcomes with regressions among the random
effects

SUMMARY OF ANALYSIS

Number of groups                                                 1
Number of observations                                         500

Number of dependent variables                                    8
Number of independent variables                                  0
Number of continuous latent variables                            4

Observed dependent variables

  Continuous
   Y11         Y12         Y13         Y14         Y21         Y22
   Y23         Y24

Continuous latent variables
   I1          S1          I2          S2


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)
  ex6.13.dat

Input data format  FREE



THE MODEL ESTIMATION TERMINATED NORMALLY



MODEL FIT INFORMATION

Number of Free Parameters                       20

Loglikelihood

          H0 Value                       -5990.576
          H1 Value                       -5974.993

Information Criteria

          Akaike (AIC)                   12021.152
          Bayesian (BIC)                 12105.445
          Sample-Size Adjusted BIC       12041.963
            (n* = (n + 2) / 24)

Chi-Square Test of Model Fit

          Value                             31.167
          Degrees of Freedom                    24
          P-Value                           0.1490

RMSEA (Root Mean Square Error Of Approximation)

          Estimate                           0.024
          90 Percent C.I.                    0.000  0.046
          Probability RMSEA <= .05           0.976

CFI/TLI

          CFI                                0.998
          TLI                                0.997

Chi-Square Test of Model Fit for the Baseline Model

          Value                           3333.622
          Degrees of Freedom                    28
          P-Value                           0.0000

SRMR (Standardized Root Mean Square Residual)

          Value                              0.017



MODEL RESULTS

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

 I1       |
    Y11                1.000      0.000    999.000    999.000
    Y12                1.000      0.000    999.000    999.000
    Y13                1.000      0.000    999.000    999.000
    Y14                1.000      0.000    999.000    999.000

 S1       |
    Y11                0.000      0.000    999.000    999.000
    Y12                1.000      0.000    999.000    999.000
    Y13                2.000      0.000    999.000    999.000
    Y14                3.000      0.000    999.000    999.000

 I2       |
    Y21                1.000      0.000    999.000    999.000
    Y22                1.000      0.000    999.000    999.000
    Y23                1.000      0.000    999.000    999.000
    Y24                1.000      0.000    999.000    999.000

 S2       |
    Y21                0.000      0.000    999.000    999.000
    Y22                1.000      0.000    999.000    999.000
    Y23                2.000      0.000    999.000    999.000
    Y24                3.000      0.000    999.000    999.000

 S1       ON
    I2                 0.329      0.025     13.197      0.000

 S2       ON
    I1                 0.602      0.029     20.653      0.000

 I2       WITH
    I1                 0.025      0.057      0.443      0.658

 S2       WITH
    S1                 0.081      0.019      4.360      0.000

 Means
    I1                 0.531      0.052     10.261      0.000
    I2                 0.496      0.052      9.577      0.000

 Intercepts
    Y11                0.000      0.000    999.000    999.000
    Y12                0.000      0.000    999.000    999.000
    Y13                0.000      0.000    999.000    999.000
    Y14                0.000      0.000    999.000    999.000
    Y21                0.000      0.000    999.000    999.000
    Y22                0.000      0.000    999.000    999.000
    Y23                0.000      0.000    999.000    999.000
    Y24                0.000      0.000    999.000    999.000
    S1                 0.994      0.029     33.940      0.000
    S2                 1.011      0.032     31.327      0.000

 Variances
    I1                 0.988      0.083     11.964      0.000
    I2                 1.018      0.084     12.119      0.000

 Residual Variances
    Y11                0.497      0.055      9.049      0.000
    Y12                0.527      0.042     12.659      0.000
    Y13                0.547      0.049     11.271      0.000
    Y14                0.493      0.081      6.066      0.000
    Y21                0.453      0.057      7.981      0.000
    Y22                0.489      0.040     12.085      0.000
    Y23                0.449      0.043     10.483      0.000
    Y24                0.466      0.076      6.156      0.000
    S1                 0.218      0.022      9.893      0.000
    S2                 0.179      0.026      6.913      0.000


QUALITY OF NUMERICAL RESULTS

     Condition Number for the Information Matrix              0.876E-02
       (ratio of smallest to largest eigenvalue)


     Beginning Time:  22:55:47
        Ending Time:  22:55:47
       Elapsed Time:  00:00:00



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