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

INPUT INSTRUCTIONS

  TITLE:	this is an example of a two-level
  	multiple group CFA with continuous
  	factor indicators
  DATA: 	FILE IS ex9.11.dat;
  VARIABLE:	NAMES ARE y1-y6 g clus;
  	GROUPING = g (1 = g1 2 = g2);
  	CLUSTER = clus;
  ANALYSIS:	TYPE = TWOLEVEL;
  MODEL:	
  	%WITHIN%
  	fw1 BY y1-y3;
  	fw2 BY y4-y6;
  	%BETWEEN%
  	fb1 BY y1-y3;
  	fb2 BY y4-y6;
  MODEL g2:
  	%WITHIN%
  	fw1 BY y2-y3;
  	fw2 BY y5-y6;



INPUT READING TERMINATED NORMALLY



this is an example of a two-level
multiple group CFA with continuous
factor indicators

SUMMARY OF ANALYSIS

Number of groups                                                 2
Number of observations
   Group G1                                                   1000
   Group G2                                                    800
   Total sample size                                          1800

Number of dependent variables                                    6
Number of independent variables                                  0
Number of continuous latent variables                            4

Observed dependent variables

  Continuous
   Y1          Y2          Y3          Y4          Y5          Y6

Continuous latent variables
   FW1         FW2         FB1         FB2

Variables with special functions

  Grouping variable     G
  Cluster variable      CLUS

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)
  ex9.11.dat
Input data format  FREE


SUMMARY OF DATA

   Group G1
     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           0.234      Y2           0.223      Y3           0.177
     Y4           0.192      Y5           0.171      Y6           0.220


   Group G2
     Number of clusters                        120

     Average cluster size        6.667

     Estimated Intraclass Correlations for the Y Variables

                Intraclass              Intraclass              Intraclass
     Variable  Correlation   Variable  Correlation   Variable  Correlation

     Y1           0.227      Y2           0.276      Y3           0.236
     Y4           0.221      Y5           0.234      Y6           0.215




THE MODEL ESTIMATION TERMINATED NORMALLY



MODEL FIT INFORMATION

Number of Free Parameters                       56

Loglikelihood

          H0 Value                      -24926.956
          H0 Scaling Correction Factor      1.0118
            for MLR
          H1 Value                      -24909.987
          H1 Scaling Correction Factor      0.9597
            for MLR

Information Criteria

          Akaike (AIC)                   49965.913
          Bayesian (BIC)                 50273.663
          Sample-Size Adjusted BIC       50095.754
            (n* = (n + 2) / 24)

Chi-Square Test of Model Fit

          Value                             38.271*
          Degrees of Freedom                    40
          P-Value                           0.5483
          Scaling Correction Factor         0.8868
            for MLR

Chi-Square Contribution From Each Group

          G1                                15.435
          G2                                22.835

*   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                                1.002

Chi-Square Test of Model Fit for the Baseline Model

          Value                           1130.168
          Degrees of Freedom                    60
          P-Value                           0.0000

SRMR (Standardized Root Mean Square Residual)

          Value for Within                   0.014
          Value for Between                  0.099



MODEL RESULTS

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

Group G1

Within Level

 FW1      BY
    Y1                 1.000      0.000    999.000    999.000
    Y2                 1.129      0.117      9.681      0.000
    Y3                 1.218      0.132      9.233      0.000

 FW2      BY
    Y4                 1.000      0.000    999.000    999.000
    Y5                 0.969      0.107      9.090      0.000
    Y6                 1.083      0.146      7.412      0.000

 FW2      WITH
    FW1                0.948      0.151      6.264      0.000

 Variances
    FW1                1.590      0.254      6.261      0.000
    FW2                1.795      0.310      5.797      0.000

 Residual Variances
    Y1                 4.139      0.246     16.841      0.000
    Y2                 4.089      0.252     16.205      0.000
    Y3                 3.898      0.291     13.399      0.000
    Y4                 3.953      0.277     14.250      0.000
    Y5                 4.715      0.240     19.650      0.000
    Y6                 3.797      0.271     13.991      0.000

Between Level

 FB1      BY
    Y1                 1.000      0.000    999.000    999.000
    Y2                 1.382      0.893      1.547      0.122
    Y3                 0.587      0.172      3.404      0.001

 FB2      BY
    Y4                 1.000      0.000    999.000    999.000
    Y5                 0.799      0.702      1.138      0.255
    Y6                 0.942      1.056      0.893      0.372

 FB2      WITH
    FB1                0.053      0.219      0.244      0.807

 Means
    FB1                0.000      0.000    999.000    999.000
    FB2                0.000      0.000    999.000    999.000

 Intercepts
    Y1                 0.046      0.131      0.349      0.727
    Y2                -0.044      0.148     -0.299      0.765
    Y3                 0.078      0.108      0.721      0.471
    Y4                -0.130      0.125     -1.037      0.300
    Y5                -0.223      0.127     -1.756      0.079
    Y6                 0.076      0.143      0.535      0.593

 Variances
    FB1                0.707      0.530      1.335      0.182
    FB2                0.462      0.594      0.777      0.437

 Residual Variances
    Y1                 0.984      0.496      1.983      0.047
    Y2                 0.439      0.820      0.535      0.593
    Y3                 1.071      0.231      4.626      0.000
    Y4                 0.883      0.505      1.749      0.080
    Y5                 0.964      0.269      3.587      0.000
    Y6                 1.329      0.473      2.808      0.005

Group G2

Within Level

 FW1      BY
    Y1                 1.000      0.000    999.000    999.000
    Y2                 0.576      0.107      5.395      0.000
    Y3                 0.519      0.100      5.198      0.000

 FW2      BY
    Y4                 1.000      0.000    999.000    999.000
    Y5                 0.690      0.100      6.901      0.000
    Y6                 0.754      0.093      8.121      0.000

 FW2      WITH
    FW1                1.054      0.214      4.934      0.000

 Variances
    FW1                2.530      0.512      4.938      0.000
    FW2                2.663      0.421      6.321      0.000

 Residual Variances
    Y1                 3.484      0.532      6.554      0.000
    Y2                 3.483      0.217     16.068      0.000
    Y3                 4.072      0.238     17.139      0.000
    Y4                 3.113      0.368      8.458      0.000
    Y5                 4.200      0.312     13.454      0.000
    Y6                 3.808      0.250     15.256      0.000

Between Level

 FB1      BY
    Y1                 1.000      0.000    999.000    999.000
    Y2                 1.382      0.893      1.547      0.122
    Y3                 0.587      0.172      3.404      0.001

 FB2      BY
    Y4                 1.000      0.000    999.000    999.000
    Y5                 0.799      0.702      1.138      0.255
    Y6                 0.942      1.056      0.893      0.372

 FB2      WITH
    FB1                0.042      0.279      0.151      0.880

 Means
    FB1               -0.015      0.148     -0.098      0.922
    FB2                0.096      0.163      0.586      0.558

 Intercepts
    Y1                 0.046      0.131      0.349      0.727
    Y2                -0.044      0.148     -0.299      0.765
    Y3                 0.078      0.108      0.721      0.471
    Y4                -0.130      0.125     -1.037      0.300
    Y5                -0.223      0.127     -1.756      0.079
    Y6                 0.076      0.143      0.535      0.593

 Variances
    FB1                0.656      0.387      1.696      0.090
    FB2                0.408      0.544      0.750      0.453

 Residual Variances
    Y1                 1.162      0.422      2.756      0.006
    Y2                 0.399      0.922      0.432      0.665
    Y3                 1.244      0.280      4.449      0.000
    Y4                 1.210      0.496      2.441      0.015
    Y5                 1.452      0.323      4.502      0.000
    Y6                 1.052      0.409      2.571      0.010


QUALITY OF NUMERICAL RESULTS

     Condition Number for the Information Matrix              0.236E-02
       (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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