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

INPUT INSTRUCTIONS

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



INPUT READING TERMINATED NORMALLY



this is an example of a linear 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                            2

Observed dependent variables

  Continuous
   Y11         Y12         Y13         Y14

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)
  ex6.1.dat

Input data format  FREE



THE MODEL ESTIMATION TERMINATED NORMALLY



MODEL FIT INFORMATION

Number of Free Parameters                        9

Loglikelihood

          H0 Value                       -3016.386
          H1 Value                       -3014.089

Information Criteria

          Akaike (AIC)                    6050.772
          Bayesian (BIC)                  6088.703
          Sample-Size Adjusted BIC        6060.137
            (n* = (n + 2) / 24)

Chi-Square Test of Model Fit

          Value                              4.593
          Degrees of Freedom                     5
          P-Value                           0.4675

RMSEA (Root Mean Square Error Of Approximation)

          Estimate                           0.000
          90 Percent C.I.                    0.000  0.060
          Probability RMSEA <= .05           0.894

CFI/TLI

          CFI                                1.000
          TLI                                1.000

Chi-Square Test of Model Fit for the Baseline Model

          Value                           1439.722
          Degrees of Freedom                     6
          P-Value                           0.0000

SRMR (Standardized Root Mean Square Residual)

          Value                              0.010



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

 S        WITH
    I                  0.133      0.032      4.100      0.000

 Means
    I                  0.523      0.051     10.152      0.000
    S                  1.026      0.025     40.264      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                  0.989      0.088     11.178      0.000
    S                  0.224      0.022     10.068      0.000

 Residual Variances
    Y11                0.475      0.058      8.122      0.000
    Y12                0.482      0.040     11.980      0.000
    Y13                0.473      0.047     10.080      0.000
    Y14                0.545      0.083      6.593      0.000


QUALITY OF NUMERICAL RESULTS

     Condition Number for the Information Matrix              0.462E-01
       (ratio of smallest to largest eigenvalue)


     Beginning Time:  22:55:46
        Ending Time:  22:55:46
       Elapsed Time:  00:00:00



MUTHEN & MUTHEN
3463 Stoner Ave.
Los Angeles, CA  90066

Tel: (310) 391-9971
Fax: (310) 391-8971
Web: www.StatModel.com
Support: Support@StatModel.com

Copyright (c) 1998-2012 Muthen & Muthen

Back to examples