Mplus VERSION 6 MUTHEN & MUTHEN 07/06/2010 9:43 AM INPUT INSTRUCTIONS title: M1 model variable: NAMES ARE y1-y5; GROUPING IS y5 (0 = g1 1 = g2); data: file=2.dat; analysis: estimator=mlr; model: f1 BY y1-y4; model g2: f1 BY y1@1 y2-y4; INPUT READING TERMINATED NORMALLY M1 model SUMMARY OF ANALYSIS Number of groups 2 Number of observations Group G1 72 Group G2 28 Number of dependent variables 4 Number of independent variables 0 Number of continuous latent variables 1 Observed dependent variables Continuous Y1 Y2 Y3 Y4 Continuous latent variables F1 Variables with special functions Grouping variable 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) 2.dat Input data format FREE THE MODEL ESTIMATION TERMINATED NORMALLY TESTS OF MODEL FIT Chi-Square Test of Model Fit Value 23.536* Degrees of Freedom 7 P-Value 0.0014 Scaling Correction Factor 0.941 for MLR Chi-Square Contributions From Each Group G1 7.139 G2 16.397 * 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 67.395 Degrees of Freedom 12 P-Value 0.0000 CFI/TLI CFI 0.701 TLI 0.488 Loglikelihood H0 Value -330.173 H0 Scaling Correction Factor 1.471 for MLR H1 Value -319.094 H1 Scaling Correction Factor 1.339 for MLR Information Criteria Number of Free Parameters 21 Akaike (AIC) 702.347 Bayesian (BIC) 757.055 Sample-Size Adjusted BIC 690.732 (n* = (n + 2) / 24) RMSEA (Root Mean Square Error Of Approximation) Estimate 0.217 90 Percent C.I. 0.125 0.317 Probability RMSEA <= .05 0.004 SRMR (Standardized Root Mean Square Residual) Value 0.116 MODEL RESULTS Two-Tailed Estimate S.E. Est./S.E. P-Value Group G1 F1 BY Y1 1.000 0.000 999.000 999.000 Y2 0.527 0.411 1.283 0.200 Y3 2.128 0.791 2.690 0.007 Y4 1.881 0.899 2.092 0.036 Means F1 0.000 0.000 999.000 999.000 Intercepts Y1 0.102 0.037 2.768 0.006 Y2 0.236 0.052 4.584 0.000 Y3 0.267 0.067 4.022 0.000 Y4 0.295 0.064 4.590 0.000 Variances F1 0.050 0.023 2.223 0.026 Residual Variances Y1 0.049 0.016 3.133 0.002 Y2 0.159 0.029 5.420 0.000 Y3 0.346 0.128 2.697 0.007 Y4 0.336 0.112 3.002 0.003 Group G2 F1 BY Y1 1.000 0.000 999.000 999.000 Y2 0.447 0.195 2.291 0.022 Y3 0.725 0.117 6.211 0.000 Y4 0.860 0.177 4.867 0.000 Means F1 0.399 0.207 1.927 0.054 Intercepts Y1 0.102 0.037 2.768 0.006 Y2 0.236 0.052 4.584 0.000 Y3 0.267 0.067 4.022 0.000 Y4 0.295 0.064 4.590 0.000 Variances F1 0.715 0.325 2.202 0.028 Residual Variances Y1 0.563 0.235 2.395 0.017 Y2 1.010 0.368 2.741 0.006 Y3 0.130 0.056 2.333 0.020 Y4 0.247 0.120 2.064 0.039 QUALITY OF NUMERICAL RESULTS Condition Number for the Information Matrix 0.192E-03 (ratio of smallest to largest eigenvalue) Beginning Time: 09:43:10 Ending Time: 09:43:10 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