Mplus VERSION 7.3
MUTHEN & MUTHEN
09/22/2014   5:58 PM

INPUT INSTRUCTIONS

  TITLE:	this is an example of a two-level growth
  	model for a continuous outcome (three-
  	level analysis)
  DATA:	FILE IS ex9.12.dat;
  VARIABLE:	NAMES ARE y1-y4 x w clus;
  	WITHIN = x;
  	BETWEEN = w;
  	CLUSTER = clus;
  ANALYSIS:	TYPE = TWOLEVEL;
  MODEL:
  	%WITHIN%
  	iw sw | y1@0 y2@1 y3@2 y4@3;
  	y1-y4 (1);
  	iw sw ON x;
  	%BETWEEN%
  	ib sb | y1@0 y2@1 y3@2 y4@3;
  	y1-y4@0;
  	ib sb ON w;



INPUT READING TERMINATED NORMALLY



this is an example of a two-level growth
model for a continuous outcome (three-
level analysis)

SUMMARY OF ANALYSIS

Number of groups                                                 1
Number of observations                                        1000

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

Observed dependent variables

  Continuous
   Y1          Y2          Y3          Y4

Observed independent variables
   X           W

Continuous latent variables
   IW          SW          IB          SB

Variables with special functions

  Cluster variable      CLUS

  Within variables
   X

  Between variables
   W


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)
  ex9.12.dat
Input data format  FREE


SUMMARY OF DATA

     Number of clusters                        110

     Average cluster size        9.091

     Estimated Intraclass Correlations for the Y Variables

                Intraclass              Intraclass              Intraclass
     Variable  Correlation   Variable  Correlation   Variable  Correlation

     Y1           0.242      Y2           0.298      Y3           0.337
     Y4           0.339




THE MODEL ESTIMATION TERMINATED NORMALLY



MODEL FIT INFORMATION

Number of Free Parameters                       13

Loglikelihood

          H0 Value                       -6531.551
          H0 Scaling Correction Factor      0.9574
            for MLR
          H1 Value                       -6522.817
          H1 Scaling Correction Factor      0.7826
            for MLR

Information Criteria

          Akaike (AIC)                   13089.103
          Bayesian (BIC)                 13152.904
          Sample-Size Adjusted BIC       13111.615
            (n* = (n + 2) / 24)

Chi-Square Test of Model Fit

          Value                             26.346*
          Degrees of Freedom                    19
          P-Value                           0.1208
          Scaling Correction Factor         0.6630
            for MLR

*   The chi-square value for MLM, MLMV, MLR, ULSMV, WLSM and WLSMV cannot be used
    for chi-square difference testing in the regular way.  MLM, MLR and WLSM
    chi-square difference testing is described on the Mplus website.  MLMV, WLSMV,
    and ULSMV difference testing is done using the DIFFTEST option.

RMSEA (Root Mean Square Error Of Approximation)

          Estimate                           0.020

CFI/TLI

          CFI                                0.999
          TLI                                0.999

Chi-Square Test of Model Fit for the Baseline Model

          Value                           7171.664
          Degrees of Freedom                    20
          P-Value                           0.0000

SRMR (Standardized Root Mean Square Residual)

          Value for Within                   0.008
          Value for Between                  0.006



MODEL RESULTS

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

Within Level

 IW       |
    Y1                 1.000      0.000    999.000    999.000
    Y2                 1.000      0.000    999.000    999.000
    Y3                 1.000      0.000    999.000    999.000
    Y4                 1.000      0.000    999.000    999.000

 SW       |
    Y1                 0.000      0.000    999.000    999.000
    Y2                 1.000      0.000    999.000    999.000
    Y3                 2.000      0.000    999.000    999.000
    Y4                 3.000      0.000    999.000    999.000

 IW         ON
    X                  1.006      0.038     26.576      0.000

 SW         ON
    X                  0.167      0.027      6.299      0.000

 SW       WITH
    IW                 0.022      0.032      0.680      0.497

 Residual Variances
    Y1                 0.511      0.015     33.838      0.000
    Y2                 0.511      0.015     33.838      0.000
    Y3                 0.511      0.015     33.838      0.000
    Y4                 0.511      0.015     33.838      0.000
    IW                 1.024      0.070     14.672      0.000
    SW                 0.473      0.025     19.087      0.000

Between Level

 IB       |
    Y1                 1.000      0.000    999.000    999.000
    Y2                 1.000      0.000    999.000    999.000
    Y3                 1.000      0.000    999.000    999.000
    Y4                 1.000      0.000    999.000    999.000

 SB       |
    Y1                 0.000      0.000    999.000    999.000
    Y2                 1.000      0.000    999.000    999.000
    Y3                 2.000      0.000    999.000    999.000
    Y4                 3.000      0.000    999.000    999.000

 IB         ON
    W                  0.574      0.080      7.126      0.000

 SB         ON
    W                  0.227      0.042      5.460      0.000

 SB       WITH
    IB                 0.023      0.042      0.542      0.588

 Intercepts
    Y1                 0.000      0.000    999.000    999.000
    Y2                 0.000      0.000    999.000    999.000
    Y3                 0.000      0.000    999.000    999.000
    Y4                 0.000      0.000    999.000    999.000
    IB                 1.059      0.076     13.841      0.000
    SB                 0.461      0.051      9.041      0.000

 Residual Variances
    Y1                 0.000      0.000    999.000    999.000
    Y2                 0.000      0.000    999.000    999.000
    Y3                 0.000      0.000    999.000    999.000
    Y4                 0.000      0.000    999.000    999.000
    IB                 0.459      0.080      5.701      0.000
    SB                 0.214      0.035      6.062      0.000


QUALITY OF NUMERICAL RESULTS

     Condition Number for the Information Matrix              0.335E-01
       (ratio of smallest to largest eigenvalue)


     Beginning Time:  17:58:55
        Ending Time:  17:58:55
       Elapsed Time:  00:00:00



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