Mplus VERSION 6 MUTHEN & MUTHEN 07/01/2010 10:53 AM INPUT INSTRUCTIONS title: M1 model variable: NAMES ARE y1-y5; data: file=1.dat; analysis: estimator=mlr; model: y1-y5 with y1-y5; *** WARNING in MODEL command Duplicate: Y2 WITH Y1 *** WARNING in MODEL command Duplicate: Y3 WITH Y1 *** WARNING in MODEL command Duplicate: Y3 WITH Y2 *** WARNING in MODEL command Duplicate: Y4 WITH Y1 *** WARNING in MODEL command Duplicate: Y4 WITH Y2 *** WARNING in MODEL command Duplicate: Y4 WITH Y3 *** WARNING in MODEL command Duplicate: Y5 WITH Y1 *** WARNING in MODEL command Duplicate: Y5 WITH Y2 *** WARNING in MODEL command Duplicate: Y5 WITH Y3 *** WARNING in MODEL command Duplicate: Y5 WITH Y4 *** WARNING in MODEL command Y1 WITH Y1 has no effect. *** WARNING in MODEL command Y2 WITH Y2 has no effect. *** WARNING in MODEL command Y3 WITH Y3 has no effect. *** WARNING in MODEL command Y4 WITH Y4 has no effect. *** WARNING in MODEL command Y5 WITH Y5 has no effect. 15 WARNING(S) FOUND IN THE INPUT INSTRUCTIONS M1 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 0 Observed dependent variables Continuous Y1 Y2 Y3 Y4 Y5 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 0.000* Degrees of Freedom 0 P-Value 0.0000 Scaling Correction Factor 1.000 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 1.000 TLI 1.000 Loglikelihood H0 Value -246.017 H0 Scaling Correction Factor 1.578 for MLR H1 Value -246.017 H1 Scaling Correction Factor 1.578 for MLR Information Criteria Number of Free Parameters 20 Akaike (AIC) 532.034 Bayesian (BIC) 570.274 Sample-Size Adjusted BIC 507.498 (n* = (n + 2) / 24) RMSEA (Root Mean Square Error Of Approximation) Estimate 0.000 90 Percent C.I. 0.000 0.000 Probability RMSEA <= .05 0.000 SRMR (Standardized Root Mean Square Residual) Value 0.000 MODEL RESULTS Two-Tailed Estimate S.E. Est./S.E. P-Value Y1 WITH Y2 0.134 0.096 1.387 0.165 Y3 0.228 0.112 2.031 0.042 Y4 0.242 0.155 1.568 0.117 Y5 0.222 0.145 1.528 0.126 Y2 WITH Y3 0.088 0.073 1.207 0.227 Y4 0.100 0.098 1.020 0.308 Y5 0.240 0.123 1.956 0.050 Y3 WITH Y4 0.292 0.166 1.764 0.078 Y5 0.112 0.101 1.110 0.267 Y4 WITH Y5 0.164 0.144 1.139 0.255 Means 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 Y1 0.442 0.171 2.587 0.010 Y2 0.436 0.147 2.966 0.003 Y3 0.560 0.161 3.483 0.000 Y4 0.604 0.191 3.160 0.002 Y5 0.604 0.191 3.160 0.002 QUALITY OF NUMERICAL RESULTS Condition Number for the Information Matrix 0.280E-01 (ratio of smallest to largest eigenvalue) Beginning Time: 10:53:56 Ending Time: 10:53:56 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