Mplus VERSION 7
MUTHEN & MUTHEN
09/22/2012  11:18 PM

INPUT INSTRUCTIONS

  TITLE:	this is an example of a linear
  growth model for a continuous outcome
  with time-invariant and time-varying
  covariates carried out as a two-level
  growth model using the DATA WIDETOLONG command
  DATA:	FILE IS ex9.16.dat;
  DATA WIDETOLONG:
  	WIDE = y11-y14 | a31-a34;
  	LONG = y | a3;
  	IDVARIABLE = person;
  	REPETITION = time;
  VARIABLE:	NAMES ARE y11-y14 x1 x2 a31-a34;
  	USEVARIABLE = x1 x2 y a3 person time;
  	CLUSTER = person;
  	WITHIN = time a3;
  	BETWEEN = x1 x2;
  ANALYSIS:	TYPE = TWOLEVEL RANDOM;
  MODEL:	%WITHIN%
  	s | y ON time;
  	y ON a3;
  	%BETWEEN%
  	y s on x1 x2;
  	y WITH s;	



INPUT READING TERMINATED NORMALLY



this is an example of a linear
growth model for a continuous outcome
with time-invariant and time-varying
covariates carried out as a two-level
growth model using the DATA WIDETOLONG command

SUMMARY OF ANALYSIS

Number of groups                                                 1
Number of observations                                        2000

Number of dependent variables                                    1
Number of independent variables                                  4
Number of continuous latent variables                            1

Observed dependent variables

  Continuous
   Y

Observed independent variables
   X1          X2          A3          TIME

Continuous latent variables
   S

Variables with special functions

  Cluster variable      PERSON

  Within variables
   A3          TIME

  Between variables
   X1          X2


Estimator                                                      MLR
Information matrix                                        OBSERVED
Maximum number of iterations                                   100
Convergence criterion                                    0.100D-05
Maximum number of EM iterations                                500
Convergence criteria for the EM algorithm
  Loglikelihood change                                   0.100D-02
  Relative loglikelihood change                          0.100D-05
  Derivative                                             0.100D-03
Minimum variance                                         0.100D-03
Maximum number of steepest descent iterations                   20
Maximum number of iterations for H1                           2000
Convergence criterion for H1                             0.100D-03
Optimization algorithm                                         EMA

Input data file(s)
  ex9.16.dat
Input data format  FREE


SUMMARY OF DATA

     Number of clusters                        500

     Average cluster size        4.000

     Estimated Intraclass Correlations for the Y Variables

                Intraclass              Intraclass
     Variable  Correlation   Variable  Correlation

     Y            0.615




THE MODEL ESTIMATION TERMINATED NORMALLY



MODEL FIT INFORMATION

Number of Free Parameters                       11

Loglikelihood

          H0 Value                       -3075.852
          H0 Scaling Correction Factor      1.0035
            for MLR

Information Criteria

          Akaike (AIC)                    6173.704
          Bayesian (BIC)                  6235.314
          Sample-Size Adjusted BIC        6200.366
            (n* = (n + 2) / 24)



MODEL RESULTS

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

Within Level

 Y          ON
    A3                 0.297      0.022     13.294      0.000

 Residual Variances
    Y                  0.542      0.024     22.155      0.000

Between Level

 S          ON
    X1                 0.263      0.027      9.801      0.000
    X2                 0.473      0.025     18.909      0.000

 Y          ON
    X1                 0.561      0.054     10.296      0.000
    X2                 0.717      0.054     13.264      0.000

 Y        WITH
    S                  0.051      0.033      1.567      0.117

 Intercepts
    Y                  0.570      0.055     10.400      0.000
    S                  1.010      0.025     39.763      0.000

 Residual Variances
    Y                  1.079      0.093     11.622      0.000
    S                  0.203      0.020     10.237      0.000


QUALITY OF NUMERICAL RESULTS

     Condition Number for the Information Matrix              0.379E-01
       (ratio of smallest to largest eigenvalue)


     Beginning Time:  23:18:27
        Ending Time:  23:18:28
       Elapsed Time:  00:00:01



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