Mplus VERSION 8
MUTHEN & MUTHEN
04/10/2017   5:03 AM

INPUT INSTRUCTIONS

  TITLE:	this is an example of a two-level
  	regression analysis for a continuous
  	dependent variable with a random slope and a latent covariate
  DATA:   FILE = ex9.2c.dat;
  VARIABLE:	NAMES = y x w clus;
  	BETWEEN = w;
  	CLUSTER = clus;
  ANALYSIS:	TYPE = TWOLEVEL RANDOM;
  MODEL:
  	%WITHIN%	
   	s | y ON x;		
  	%BETWEEN%	
  	y s ON w x;
  	y WITH s;



INPUT READING TERMINATED NORMALLY



this is an example of a two-level
regression analysis for a continuous
dependent variable with a random slope and a latent covariate

SUMMARY OF ANALYSIS

Number of groups                                                 1
Number of observations                                        1000

Number of dependent variables                                    1
Number of independent variables                                  2
Number of continuous latent variables                            1

Observed dependent variables

  Continuous
   Y

Observed independent variables
   X           W

Continuous latent variables
   S

Variables with special functions

  Cluster variable      CLUS

  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)
  ex9.2c.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
     Variable  Correlation   Variable  Correlation

     Y            0.626




UNIVARIATE SAMPLE STATISTICS


     UNIVARIATE HIGHER-ORDER MOMENT DESCRIPTIVE STATISTICS

         Variable/         Mean/     Skewness/   Minimum/ % with                Percentiles
        Sample Size      Variance    Kurtosis    Maximum  Min/Max      20%/60%    40%/80%    Median

     Y                     2.045       1.000      -4.224    0.10%      -0.163      1.203      1.828
            1000.000       7.046       2.558      17.676    0.10%       2.467      3.887
     X                    -0.095      -0.056      -3.654    0.10%      -1.113     -0.368     -0.092
            1000.000       1.401      -0.311       3.140    0.10%       0.212      0.923
     W                    -0.106      -0.067      -2.364    0.91%      -0.879     -0.365     -0.079
             110.000       0.808       0.110       2.177    0.91%       0.124      0.513


THE MODEL ESTIMATION TERMINATED NORMALLY



MODEL FIT INFORMATION

Number of Free Parameters                       10

Loglikelihood

          H0 Value                       -3088.493
          H0 Scaling Correction Factor      0.9978
            for MLR

Information Criteria

          Akaike (AIC)                    6196.986
          Bayesian (BIC)                  6246.063
          Sample-Size Adjusted BIC        6214.303
            (n* = (n + 2) / 24)



MODEL RESULTS

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

Within Level

 Residual Variances
    Y                  1.026      0.052     19.706      0.000

Between Level

 S          ON
    W                  0.569      0.094      6.087      0.000
    X                  0.315      0.180      1.752      0.080

 Y          ON
    W                  1.186      0.113     10.453      0.000
    X                  1.024      0.217      4.719      0.000

 Y        WITH
    S                  0.268      0.061      4.392      0.000

 Intercepts
    Y                  2.087      0.083     25.173      0.000
    S                  1.017      0.071     14.415      0.000

 Residual Variances
    Y                  0.483      0.098      4.950      0.000
    S                  0.368      0.056      6.547      0.000


QUALITY OF NUMERICAL RESULTS

     Condition Number for the Information Matrix              0.112E-01
       (ratio of smallest to largest eigenvalue)


     Beginning Time:  05:03:49
        Ending Time:  05:03:49
       Elapsed Time:  00:00:00



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