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 an observed covariate
  DATA:	FILE = ex9.2a.dat;
  VARIABLE:	NAMES = y x w xm clus;
  	WITHIN = x;
  	BETWEEN = w xm;
  	CLUSTER = clus;
  DEFINE:	CENTER x (GROUPMEAN);
  ANALYSIS:	TYPE = TWOLEVEL RANDOM;
  MODEL:
  	%WITHIN%	
  	s | y ON x;
  	%BETWEEN%	
  	y s ON w xm;
  	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 an observed covariate

SUMMARY OF ANALYSIS

Number of groups                                                 1
Number of observations                                        1000

Number of dependent variables                                    1
Number of independent variables                                  3
Number of continuous latent variables                            1

Observed dependent variables

  Continuous
   Y

Observed independent variables
   X           W           XM

Continuous latent variables
   S

Variables with special functions

  Cluster variable      CLUS

  Within variables
   X

  Between variables
   W           XM

  Centering (GROUPMEAN)
   X


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)
  ex9.2a.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            0.587




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                     1.493       0.265      -5.451    0.10%      -0.438      0.866      1.359
            1000.000       5.547       0.223       9.654    0.10%       1.895      3.342
     X                     0.000       0.003      -2.949    0.10%      -0.802     -0.227      0.001
            1000.000       0.953       0.066       3.161    0.10%       0.213      0.811
     W                    -0.328      -0.091      -3.044    0.91%      -1.079     -0.629     -0.313
             110.000       0.885       0.005       2.076    0.91%      -0.087      0.518
     XM                   -0.277       0.224      -2.034    0.91%      -0.946     -0.479     -0.258
             110.000       0.458       0.069       1.919    0.91%      -0.133      0.304


THE MODEL ESTIMATION TERMINATED NORMALLY



MODEL FIT INFORMATION

Number of Free Parameters                       10

Loglikelihood

          H0 Value                       -1596.165
          H0 Scaling Correction Factor      0.9216
            for MLR

Information Criteria

          Akaike (AIC)                    3212.331
          Bayesian (BIC)                  3261.408
          Sample-Size Adjusted BIC        3229.648
            (n* = (n + 2) / 24)



MODEL RESULTS

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

Within Level

 Residual Variances
    Y                  1.035      0.047     21.915      0.000

Between Level

 S          ON
    W                  0.412      0.097      4.236      0.000
    XM                 0.526      0.133      3.969      0.000

 Y          ON
    W                  0.968      0.127      7.603      0.000
    XM                 1.231      0.179      6.872      0.000

 Y        WITH
    S                  0.308      0.063      4.856      0.000

 Intercepts
    Y                  2.134      0.099     21.599      0.000
    S                  1.039      0.076     13.620      0.000

 Residual Variances
    Y                  0.728      0.111      6.568      0.000
    S                  0.336      0.061      5.535      0.000


QUALITY OF NUMERICAL RESULTS

     Condition Number for the Information Matrix              0.210E-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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