Mplus VERSION 7.2
MUTHEN & MUTHEN
05/07/2014   3:07 PM

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 (GRANDMEAN);
  ANALYSIS:   TYPE = TWOLEVEL RANDOM;
  MODEL:      %WITHIN%	
  	        s | y ON x;
  	        %BETWEEN%	
  	        y ON w xm;
              [s] (gam0);
              s ON w (gam1)
              xm;
          	y WITH s;
  MODEL CONSTRAINT:
               PLOT(ylow yhigh); ! moderation by w=-1 and +1 for xm=0
              LOOP(level1,-3,3,0.01); ! level1 represents variation in x

              ylow = (gam0+gam1*(-1))*level1;
              yhigh = (gam0+gam1*1)*level1;
  PLOT:       TYPE = PLOT2;





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 (GRANDMEAN)
   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            0.574




THE MODEL ESTIMATION TERMINATED NORMALLY



MODEL FIT INFORMATION

Number of Free Parameters                       10

Loglikelihood

          H0 Value                       -1584.044
          H0 Scaling Correction Factor      0.8167
            for MLR

Information Criteria

          Akaike (AIC)                    3188.088
          Bayesian (BIC)                  3237.165
          Sample-Size Adjusted BIC        3205.405
            (n* = (n + 2) / 24)



MODEL RESULTS

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

Within Level

 Residual Variances
    Y                  1.032      0.047     22.126      0.000

Between Level

 S          ON
    W                  0.396      0.097      4.063      0.000
    XM                 0.542      0.136      4.001      0.000

 Y          ON
    W                  0.874      0.118      7.387      0.000
    XM                 1.345      0.164      8.186      0.000

 Y        WITH
    S                  0.306      0.067      4.572      0.000

 Intercepts
    Y                  2.113      0.088     24.021      0.000
    S                  1.039      0.075     13.813      0.000

 Residual Variances
    Y                  0.606      0.101      5.982      0.000
    S                  0.334      0.056      5.932      0.000


QUALITY OF NUMERICAL RESULTS

     Condition Number for the Information Matrix              0.198E-05
       (ratio of smallest to largest eigenvalue)


PLOT INFORMATION

The following plots are available:

  Loop plots

     Beginning Time:  15:07:20
        Ending Time:  15:07:20
       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) 1998-2014 Muthen & Muthen

Back to examples