Mplus VERSION 7
MUTHEN & MUTHEN
09/22/2012  10:56 PM

INPUT INSTRUCTIONS

  TITLE:	this is an example of a quadratic growth
  	model for a continuous outcome
  DATA:	FILE IS ex6.9.dat;
  VARIABLE:	NAMES ARE y11-y14;
  MODEL:	i s q | y11@0 y12@1 y13@2 y14@3;



INPUT READING TERMINATED NORMALLY



this is an example of a quadratic growth
model for a continuous outcome

SUMMARY OF ANALYSIS

Number of groups                                                 1
Number of observations                                         500

Number of dependent variables                                    4
Number of independent variables                                  0
Number of continuous latent variables                            3

Observed dependent variables

  Continuous
   Y11         Y12         Y13         Y14

Continuous latent variables
   I           S           Q


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)
  ex6.9.dat

Input data format  FREE



THE MODEL ESTIMATION TERMINATED NORMALLY



MODEL FIT INFORMATION

Number of Free Parameters                       13

Loglikelihood

          H0 Value                       -3975.519
          H1 Value                       -3975.281

Information Criteria

          Akaike (AIC)                    7977.038
          Bayesian (BIC)                  8031.828
          Sample-Size Adjusted BIC        7990.565
            (n* = (n + 2) / 24)

Chi-Square Test of Model Fit

          Value                              0.475
          Degrees of Freedom                     1
          P-Value                           0.4905

RMSEA (Root Mean Square Error Of Approximation)

          Estimate                           0.000
          90 Percent C.I.                    0.000  0.104
          Probability RMSEA <= .05           0.701

CFI/TLI

          CFI                                1.000
          TLI                                1.003

Chi-Square Test of Model Fit for the Baseline Model

          Value                           1231.275
          Degrees of Freedom                     6
          P-Value                           0.0000

SRMR (Standardized Root Mean Square Residual)

          Value                              0.004



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

 Q        |
    Y11                0.000      0.000    999.000    999.000
    Y12                1.000      0.000    999.000    999.000
    Y13                4.000      0.000    999.000    999.000
    Y14                9.000      0.000    999.000    999.000

 S        WITH
    I                 -0.173      0.300     -0.577      0.564

 Q        WITH
    I                  0.101      0.080      1.269      0.204
    S                 -0.179      0.117     -1.524      0.127

 Means
    I                  0.521      0.062      8.442      0.000
    S                  1.035      0.077     13.455      0.000
    Q                  0.512      0.031     16.442      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

 Variances
    I                  1.246      0.273      4.569      0.000
    S                  0.998      0.358      2.787      0.005
    Q                  0.282      0.060      4.666      0.000

 Residual Variances
    Y11                0.685      0.265      2.589      0.010
    Y12                0.882      0.114      7.705      0.000
    Y13                0.946      0.183      5.178      0.000
    Y14                0.738      0.945      0.781      0.435


QUALITY OF NUMERICAL RESULTS

     Condition Number for the Information Matrix              0.241E-03
       (ratio of smallest to largest eigenvalue)


     Beginning Time:  22:56:58
        Ending Time:  22:56:58
       Elapsed Time:  00:00:00



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