Mplus VERSION 6 MUTHEN & MUTHEN 06/30/2010 4:37 PM INPUT INSTRUCTIONS title: M1 model variable: NAMES ARE y1-y5; data: file=1.dat; analysis: estimator=mlr; model: f1 BY y1-y5; y1 with y2; y3 with y2; INPUT READING TERMINATED NORMALLY M0 model SUMMARY OF ANALYSIS Number of groups 1 Number of observations 50 Number of dependent variables 5 Number of independent variables 0 Number of continuous latent variables 1 Observed dependent variables Continuous Y1 Y2 Y3 Y4 Y5 Continuous latent variables F1 Estimator MLR Information matrix OBSERVED Maximum number of iterations 1000 Convergence criterion 0.500D-04 Maximum number of steepest descent iterations 20 Input data file(s) 1.dat Input data format FREE THE MODEL ESTIMATION TERMINATED NORMALLY TESTS OF MODEL FIT Chi-Square Test of Model Fit Value 7.301* Degrees of Freedom 3 P-Value 0.0629 Scaling Correction Factor 1.095 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. Chi-Square Test of Model Fit for the Baseline Model Value 43.028 Degrees of Freedom 10 P-Value 0.0000 CFI/TLI CFI 0.870 TLI 0.566 Loglikelihood H0 Value -250.015 H0 Scaling Correction Factor 1.663 for MLR H1 Value -246.017 H1 Scaling Correction Factor 1.578 for MLR Information Criteria Number of Free Parameters 17 Akaike (AIC) 534.029 Bayesian (BIC) 566.534 Sample-Size Adjusted BIC 513.173 (n* = (n + 2) / 24) RMSEA (Root Mean Square Error Of Approximation) Estimate 0.169 90 Percent C.I. 0.000 0.330 Probability RMSEA <= .05 0.089 SRMR (Standardized Root Mean Square Residual) Value 0.067 MODEL RESULTS Two-Tailed Estimate S.E. Est./S.E. P-Value F1 BY Y1 1.000 0.000 999.000 999.000 Y2 0.770 0.394 1.953 0.051 Y3 0.834 0.363 2.295 0.022 Y4 0.807 0.356 2.265 0.024 Y5 0.822 0.223 3.688 0.000 Y1 WITH Y2 -0.084 0.117 -0.720 0.471 Y3 WITH Y2 -0.095 0.116 -0.823 0.411 Intercepts Y1 0.280 0.094 2.979 0.003 Y2 0.380 0.093 4.071 0.000 Y3 0.400 0.106 3.780 0.000 Y4 0.420 0.110 3.823 0.000 Y5 0.420 0.110 3.823 0.000 Variances F1 0.281 0.184 1.522 0.128 Residual Variances Y1 0.161 0.103 1.561 0.118 Y2 0.271 0.104 2.619 0.009 Y3 0.365 0.140 2.601 0.009 Y4 0.421 0.144 2.928 0.003 Y5 0.414 0.134 3.082 0.002 QUALITY OF NUMERICAL RESULTS Condition Number for the Information Matrix 0.946E-02 (ratio of smallest to largest eigenvalue) Beginning Time: 16:37:15 Ending Time: 16:37:15 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) 1998-2010 Muthen & Muthen