Mplus VERSION 7.3
MUTHEN & MUTHEN
09/22/2014   6:01 PM

INPUT INSTRUCTIONS

  TITLE:	this is an example of a two-level
  	regression analysis for a continuous
  	dependent variable with a random intercept and an observed covariate
  DATA:	FILE = ex9.1a.dat;
  VARIABLE:	NAMES = y x w xm clus;
  	WITHIN = x;
  	BETWEEN = w xm;
  	CLUSTER = clus;
  DEFINE:	CENTER x (GRANDMEAN);
  ANALYSIS:	TYPE = TWOLEVEL;
  MODEL:
  	%WITHIN%	
  	y ON x;
  	%BETWEEN%
  	y ON w xm;




INPUT READING TERMINATED NORMALLY



this is an example of a two-level
regression analysis for a continuous
dependent variable with a random intercept 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                            0

Observed dependent variables

  Continuous
   Y

Observed independent variables
   X           W           XM

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)
  ex9.1a.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
     Variable  Correlation

     Y            0.570




THE MODEL ESTIMATION TERMINATED NORMALLY



MODEL FIT INFORMATION

Number of Free Parameters                        6

Loglikelihood

          H0 Value                       -1525.938
          H0 Scaling Correction Factor      0.9402
            for MLR
          H1 Value                       -1525.938
          H1 Scaling Correction Factor      0.9402
            for MLR

Information Criteria

          Akaike (AIC)                    3063.876
          Bayesian (BIC)                  3093.322
          Sample-Size Adjusted BIC        3074.266
            (n* = (n + 2) / 24)

Chi-Square Test of Model Fit

          Value                              0.000*
          Degrees of Freedom                     0
          P-Value                           0.0000
          Scaling Correction Factor         1.0000
            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                                1.000

Chi-Square Test of Model Fit for the Baseline Model

          Value                            491.881
          Degrees of Freedom                     3
          P-Value                           0.0000

SRMR (Standardized Root Mean Square Residual)

          Value for Within                   0.000
          Value for Between                  0.000



MODEL RESULTS

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

Within Level

 Y          ON
    X                  0.724      0.033     22.118      0.000

 Residual Variances
    Y                  1.022      0.041     25.117      0.000

Between Level

 Y          ON
    W                  0.570      0.108      5.305      0.000
    XM                 0.976      0.160      6.107      0.000

 Intercepts
    Y                  1.991      0.080     24.804      0.000

 Residual Variances
    Y                  0.571      0.088      6.486      0.000


QUALITY OF NUMERICAL RESULTS

     Condition Number for the Information Matrix              0.335E-01
       (ratio of smallest to largest eigenvalue)


     Beginning Time:  18:01:21
        Ending Time:  18:01:21
       Elapsed Time:  00:00:00



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