Mplus VERSION 7.11 MUTHEN & MUTHEN 01/22/2014 9:39 PM INPUT INSTRUCTIONS TITLE: Kapitel 7, Beispiel 2: Stimmungsmessung, Latent-State-Trait-Modell für zwei Messgelegenheiten robuste Maximum-Likelihood-Schätzung DATA: FILE IS kapitel_7_beispiel_2_stimmung.dat; FORMAT IS Free; TYPE IS Individual; VARIABLE: Names are Y1-Y4; USEVARIABLES are Y1-Y4; MISSING ARE *; ANALYSIS: TYPE IS general ; ESTIMATOR IS MLR; ITERATIONS = 4000; CONVERGENCE = 0.00050; H1ITERATIONS = 10000000; H1CONVERGENCE = 0.001 Model: ! gemeinsame latente Variable (trait) eta1 by Y1 Y2@1 Y3@1 Y4@1; ! messgelegenheitsspezifische Variable ! (erster Messzeotpunkt) eta2 by Y1 Y2@1; ! messgelegenheitsspezifische Variable ! (erster Messzeotpunkt) eta3 by Y3 Y4@1; ! Unkorreliertheit der Faktoren eta1 with eta2@0 eta3@0; eta2 with eta3@0; ! Gleichheit der Varianzen der messgelegenheitsspezifischen ! Faktoren eta2 eta3 (evar); ! Gleichheit der Fehlervarinzen Y1-Y4 (fvar2); OUTPUT: stdyx; INPUT READING TERMINATED NORMALLY Kapitel 7, Beispiel 2: Stimmungsmessung, Latent-State-Trait-Modell für zwei Messgelegenheiten robuste Maximum-Likelihood-Schätzung SUMMARY OF ANALYSIS Number of groups 1 Number of observations 212 Number of dependent variables 4 Number of independent variables 0 Number of continuous latent variables 3 Observed dependent variables Continuous Y1 Y2 Y3 Y4 Continuous latent variables ETA1 ETA2 ETA3 Estimator MLR Information matrix OBSERVED Maximum number of iterations 4000 Convergence criterion 0.500D-03 Maximum number of steepest descent iterations 20 Maximum number of iterations for H1 10000000 Convergence criterion for H1 0.100D-02 Input data file(s) kapitel_7_beispiel_2_stimmung.dat Input data format FREE SUMMARY OF DATA Number of missing data patterns 1 COVARIANCE COVERAGE OF DATA Minimum covariance coverage value 0.100 PROPORTION OF DATA PRESENT Covariance Coverage Y1 Y2 Y3 Y4 ________ ________ ________ ________ Y1 1.000 Y2 1.000 1.000 Y3 1.000 1.000 1.000 Y4 1.000 1.000 1.000 1.000 THE MODEL ESTIMATION TERMINATED NORMALLY MODEL FIT INFORMATION Number of Free Parameters 7 Loglikelihood H0 Value -922.389 H0 Scaling Correction Factor 1.4608 for MLR H1 Value -919.193 H1 Scaling Correction Factor 1.4535 for MLR Information Criteria Akaike (AIC) 1858.777 Bayesian (BIC) 1882.273 Sample-Size Adjusted BIC 1860.093 (n* = (n + 2) / 24) Chi-Square Test of Model Fit Value 4.420* Degrees of Freedom 7 P-Value 0.7303 Scaling Correction Factor 1.4461 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. RMSEA (Root Mean Square Error Of Approximation) Estimate 0.000 90 Percent C.I. 0.000 0.062 Probability RMSEA <= .05 0.909 CFI/TLI CFI 1.000 TLI 1.012 Chi-Square Test of Model Fit for the Baseline Model Value 193.124 Degrees of Freedom 6 P-Value 0.0000 SRMR (Standardized Root Mean Square Residual) Value 0.088 MODEL RESULTS Two-Tailed Estimate S.E. Est./S.E. P-Value ETA1 BY Y1 1.000 0.000 999.000 999.000 Y2 1.000 0.000 999.000 999.000 Y3 1.000 0.000 999.000 999.000 Y4 1.000 0.000 999.000 999.000 ETA2 BY Y1 1.000 0.000 999.000 999.000 Y2 1.000 0.000 999.000 999.000 ETA3 BY Y3 1.000 0.000 999.000 999.000 Y4 1.000 0.000 999.000 999.000 ETA1 WITH ETA2 0.000 0.000 999.000 999.000 ETA3 0.000 0.000 999.000 999.000 ETA2 WITH ETA3 0.000 0.000 999.000 999.000 Intercepts Y1 3.880 0.064 60.523 0.000 Y2 4.059 0.058 69.451 0.000 Y3 3.960 0.060 65.874 0.000 Y4 4.066 0.061 66.789 0.000 Variances ETA1 0.410 0.067 6.153 0.000 ETA2 0.117 0.032 3.675 0.000 ETA3 0.117 0.032 3.675 0.000 Residual Variances Y1 0.259 0.031 8.362 0.000 Y2 0.259 0.031 8.362 0.000 Y3 0.259 0.031 8.362 0.000 Y4 0.259 0.031 8.362 0.000 STANDARDIZED MODEL RESULTS STDYX Standardization Two-Tailed Estimate S.E. Est./S.E. P-Value ! Man erhält den Konsistenzkoeffizienten, indem man die standardisierten Ladungen quadriert ETA1 BY Y1 0.722 0.037 19.757 0.000 Y2 0.722 0.037 19.757 0.000 Y3 0.722 0.037 19.757 0.000 Y4 0.722 0.037 19.757 0.000 ! Man erhält den Spezifitätskoeffizienten, indem man die standardisierten Ladungen quadriert ETA2 BY Y1 0.386 0.054 7.201 0.000 Y2 0.386 0.054 7.201 0.000 ETA3 BY Y3 0.386 0.054 7.201 0.000 Y4 0.386 0.054 7.201 0.000 ETA1 WITH ETA2 0.000 0.000 999.000 999.000 ETA3 0.000 0.000 999.000 999.000 ETA2 WITH ETA3 0.000 0.000 999.000 999.000 Intercepts Y1 4.374 0.230 19.037 0.000 Y2 4.576 0.235 19.490 0.000 Y3 4.464 0.230 19.408 0.000 Y4 4.584 0.239 19.173 0.000 Variances ETA1 1.000 0.000 999.000 999.000 ETA2 1.000 0.000 999.000 999.000 ETA3 1.000 0.000 999.000 999.000 ! Die Unreliabilitätskoeffizienten sind die standardisierten Residualvarianzen Residual Variances Y1 0.329 0.042 7.798 0.000 Y2 0.329 0.042 7.798 0.000 Y3 0.329 0.042 7.798 0.000 Y4 0.329 0.042 7.798 0.000 R-SQUARE Observed Two-Tailed Variable Estimate S.E. Est./S.E. P-Value Y1 0.671 0.042 15.898 0.000 Y2 0.671 0.042 15.898 0.000 Y3 0.671 0.042 15.898 0.000 Y4 0.671 0.042 15.898 0.000 QUALITY OF NUMERICAL RESULTS Condition Number for the Information Matrix 0.868E-01 (ratio of smallest to largest eigenvalue) DIAGRAM INFORMATION Use View Diagram under the Diagram menu in the Mplus Editor to view the diagram. If running Mplus from the Mplus Diagrammer, the diagram opens automatically. Diagram output c:\users\eid\documents\lehrbuch-testtheorie\kapitel\kapitel 7 -multidimensional\beispiel-lst-mod Beginning Time: 21:39:54 Ending Time: 21:39:54 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) 1998-2013 Muthen & Muthen