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

INPUT INSTRUCTIONS

  TITLE:	this is an example of a linear growth
  	model for a continuous outcome with time-
  	invariant and time-varying covariates
  DATA:	FILE IS ex6.10.dat;
  VARIABLE:	NAMES ARE y11-y14 x1 x2 a31-a34;
  MODEL:	i s | y11@0 y12@1 y13@2 y14@3;
  	i s ON x1 x2;
  	y11 ON a31;
  	y12 ON a32;
  	y13 ON a33;
  	y14 ON a34;



INPUT READING TERMINATED NORMALLY



this is an example of a linear growth
model for a continuous outcome with time-
invariant and time-varying covariates

SUMMARY OF ANALYSIS

Number of groups                                                 1
Number of observations                                         500

Number of dependent variables                                    4
Number of independent variables                                  6
Number of continuous latent variables                            2

Observed dependent variables

  Continuous
   Y11         Y12         Y13         Y14

Observed independent variables
   X1          X2          A31         A32         A33         A34

Continuous latent variables
   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)
  ex6.10.dat

Input data format  FREE



THE MODEL ESTIMATION TERMINATED NORMALLY



MODEL FIT INFORMATION

Number of Free Parameters                       17

Loglikelihood

          H0 Value                       -3070.619
          H1 Value                       -3057.726

Information Criteria

          Akaike (AIC)                    6175.239
          Bayesian (BIC)                  6246.887
          Sample-Size Adjusted BIC        6192.928
            (n* = (n + 2) / 24)

Chi-Square Test of Model Fit

          Value                             25.786
          Degrees of Freedom                    21
          P-Value                           0.2147

RMSEA (Root Mean Square Error Of Approximation)

          Estimate                           0.021
          90 Percent C.I.                    0.000  0.046
          Probability RMSEA <= .05           0.978

CFI/TLI

          CFI                                0.998
          TLI                                0.998

Chi-Square Test of Model Fit for the Baseline Model

          Value                           2862.582
          Degrees of Freedom                    30
          P-Value                           0.0000

SRMR (Standardized Root Mean Square Residual)

          Value                              0.013



MODEL RESULTS

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

 I        |
    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

 S        |
    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

 I        ON
    X1                 0.557      0.054     10.278      0.000
    X2                 0.718      0.055     12.950      0.000

 S        ON
    X1                 0.264      0.025     10.543      0.000
    X2                 0.473      0.026     18.401      0.000

 Y11      ON
    A31                0.190      0.044      4.294      0.000

 Y12      ON
    A32                0.323      0.038      8.429      0.000

 Y13      ON
    A33                0.344      0.038      8.985      0.000

 Y14      ON
    A34                0.303      0.050      6.002      0.000

 S        WITH
    I                  0.055      0.035      1.570      0.117

 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
    I                  0.570      0.054     10.465      0.000
    S                  1.010      0.025     40.075      0.000

 Residual Variances
    Y11                0.509      0.068      7.474      0.000
    Y12                0.597      0.049     12.268      0.000
    Y13                0.481      0.050      9.703      0.000
    Y14                0.579      0.089      6.492      0.000
    I                  1.074      0.097     11.122      0.000
    S                  0.201      0.022      9.001      0.000


QUALITY OF NUMERICAL RESULTS

     Condition Number for the Information Matrix              0.307E-01
       (ratio of smallest to largest eigenvalue)


     Beginning Time:  17:48:45
        Ending Time:  17:48:45
       Elapsed Time:  00:00:00



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