Mplus VERSION 4.1 MUTHEN & MUTHEN 08/08/2006 12:47 PM INPUT INSTRUCTIONS title: Introducing Multilevel Modeling by Kreft and de Leeuw. Page 85, Table 4.14 data: file = imm23.dat ; variable: names = schid stuid ses meanses homework white parented public ratio percmin math sex race sctype cstr scsize urban region; cluster = schid; usevar = math homework white public meanses; within = homework white; ! level 1 variables here between = public meanses; ! level 2 variables here analysis: type = twolevel random; estimator = ml; model: %within% math on white; ! fixed effect of white b1 | math on homework; ! random effect for homework %between% math on public meanses; ! intercept predicted from public, meanses b1; ! no predictors of b1, homework random slope math with b1; ! covariance intercept and slope INPUT READING TERMINATED NORMALLY Introducing Multilevel Modeling by Kreft and de Leeuw. Page 85, Table 4.14 SUMMARY OF ANALYSIS Number of groups 1 Number of observations 519 Number of dependent variables 1 Number of independent variables 4 Number of continuous latent variables 1 Observed dependent variables Continuous MATH Observed independent variables HOMEWORK WHITE PUBLIC MEANSES Continuous latent variables B1 Variables with special functions Cluster variable SCHID Within variables HOMEWORK WHITE Between variables PUBLIC MEANSES Estimator ML Information matrix OBSERVED Maximum number of iterations 1000 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-02 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) imm23.dat Input data format FREE SUMMARY OF DATA Number of clusters 23 Size (s) Cluster ID with Size s 5 6467 8 6327 14 72991 16 26537 17 7474 19 54344 20 7829 47583 24371 72292 25642 21 68448 68493 22 25456 7801 24725 23 7472 46417 24 7194 7930 27 72080 44 6053 67 62821 Average cluster size 22.565 Estimated Intraclass Correlations for the Y Variables Intraclass Intraclass Variable Correlation Variable Correlation MATH 0.145 THE MODEL ESTIMATION TERMINATED NORMALLY TESTS OF MODEL FIT Loglikelihood H0 Value -1808.416 Information Criteria Number of Free Parameters 9 Akaike (AIC) 3634.831 Bayesian (BIC) 3673.098 Sample-Size Adjusted BIC 3644.530 (n* = (n + 2) / 24) MODEL RESULTS Estimates S.E. Est./S.E. Within Level MATH ON WHITE 3.072 0.957 3.210 Residual Variances MATH 52.710 3.437 15.335 Between Level MATH ON PUBLIC 0.180 2.121 0.085 MEANSES 5.052 1.831 2.759 MATH WITH B1 -25.531 9.161 -2.787 Means B1 1.936 0.873 2.216 Intercepts MATH 44.637 2.140 20.861 Variances B1 15.462 5.393 2.867 Residual Variances MATH 50.158 17.467 2.872 QUALITY OF NUMERICAL RESULTS Condition Number for the Information Matrix 0.103E-03 (ratio of smallest to largest eigenvalue) Beginning Time: 12:47:08 Ending Time: 12:47:08 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) 1998-2006 Muthen & Muthen