Mplus VERSION 8.7 (Mac) MUTHEN & MUTHEN 10/21/2021 11:05 PM 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; *** WARNING in MODEL command In the MODEL command, the predictor variable on the WITHIN level refers to the whole observed variable. To use the latent within-level part, use ESTIMATOR=BAYES in the ANALYSIS command. This applies to the following statement(s): S | Y ON X 1 WARNING(S) FOUND IN THE INPUT INSTRUCTIONS 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: 23:05:46 Ending Time: 23:05:46 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) 1998-2021 Muthen & Muthen