Mplus VERSION 7
MUTHEN & MUTHEN
09/22/2012  11:34 PM

INPUT INSTRUCTIONS

  TITLE:	this is an example of a two-level CFA with
  	continuous factor indicators, a random intercept
      factor, and covariates
  DATA:	FILE IS ex9.6.dat;
  VARIABLE:	NAMES ARE y1-y4 x1 x2 w clus;
  	WITHIN = x1 x2;
  	BETWEEN = w;
  	CLUSTER = clus;
  ANALYSIS:	TYPE = TWOLEVEL;
  MODEL:
  	%WITHIN%
  	fw BY y1-y4;	
  	fw ON x1 x2;
  	%BETWEEN%
  	fb BY y1-y4;	
  	y1-y4@0;
  	fb ON w;



INPUT READING TERMINATED NORMALLY



this is an example of a two-level CFA with
continuous factor indicators, a random intercept
factor, and covariates

SUMMARY OF ANALYSIS

Number of groups                                                 1
Number of observations                                        1000

Number of dependent variables                                    4
Number of independent variables                                  3
Number of continuous latent variables                            2

Observed dependent variables

  Continuous
   Y1          Y2          Y3          Y4

Observed independent variables
   X1          X2          W

Continuous latent variables
   FW          FB

Variables with special functions

  Cluster variable      CLUS

  Within variables
   X1          X2

  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)
  ex9.6.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           0.125      Y2           0.121      Y3           0.106
     Y4           0.115




THE MODEL ESTIMATION TERMINATED NORMALLY



MODEL FIT INFORMATION

Number of Free Parameters                       19

Loglikelihood

          H0 Value                       -6544.186
          H0 Scaling Correction Factor      1.0294
            for MLR
          H1 Value                       -6542.255
          H1 Scaling Correction Factor      0.8831
            for MLR

Information Criteria

          Akaike (AIC)                   13126.372
          Bayesian (BIC)                 13219.620
          Sample-Size Adjusted BIC       13159.275
            (n* = (n + 2) / 24)

Chi-Square Test of Model Fit

          Value                              5.368*
          Degrees of Freedom                    17
          P-Value                           0.9965
          Scaling Correction Factor         0.7196
            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                           0.000

CFI/TLI

          CFI                                1.000
          TLI                                1.004

Chi-Square Test of Model Fit for the Baseline Model

          Value                           4264.565
          Degrees of Freedom                    24
          P-Value                           0.0000

SRMR (Standardized Root Mean Square Residual)

          Value for Within                   0.004
          Value for Between                  0.017



MODEL RESULTS

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

Within Level

 FW       BY
    Y1                 1.000      0.000    999.000    999.000
    Y2                 0.999      0.033     29.954      0.000
    Y3                 0.995      0.041     24.549      0.000
    Y4                 1.017      0.033     30.529      0.000

 FW         ON
    X1                 0.973      0.048     20.256      0.000
    X2                 0.510      0.039     13.017      0.000

 Residual Variances
    Y1                 0.981      0.059     16.608      0.000
    Y2                 0.947      0.054     17.464      0.000
    Y3                 1.070      0.057     18.714      0.000
    Y4                 1.014      0.059     17.193      0.000
    FW                 0.980      0.083     11.778      0.000

Between Level

 FB       BY
    Y1                 1.000      0.000    999.000    999.000
    Y2                 0.960      0.070     13.617      0.000
    Y3                 0.924      0.075     12.292      0.000
    Y4                 0.949      0.068     13.908      0.000

 FB         ON
    W                  0.344      0.079      4.339      0.000

 Intercepts
    Y1                -0.083      0.072     -1.143      0.253
    Y2                -0.077      0.073     -1.058      0.290
    Y3                -0.045      0.069     -0.647      0.518
    Y4                -0.030      0.072     -0.413      0.679

 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
    FB                 0.361      0.072      5.041      0.000


QUALITY OF NUMERICAL RESULTS

     Condition Number for the Information Matrix              0.178E-01
       (ratio of smallest to largest eigenvalue)


     Beginning Time:  23:34:53
        Ending Time:  23:34:53
       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