Mplus VERSION 7 (Mac) MUTHEN & MUTHEN 05/28/2015 4:43 PM INPUT INSTRUCTIONS DATA: FILE IS data_IQ.dat; VARIABLE: NAMES ARE IQscore ; ANALYSIS: ESTIMATOR IS bayes; MODEL: [IQscore] (priorMean); IQscore; MODEL PRIORS: !priorMean ~ N(***MEAN***, ***VARAIANCE***); OUTPUT: STAND; CINTERVAL(HPD) TECH8; PLOT: TYPE IS PLOT3; *** WARNING in MODEL command All variables are uncorrelated with all other variables in the model. Check that this is what is intended. *** WARNING Variable name contains more than 8 characters. Only the first 8 characters will be printed in the output. Variable: PRIORMEAN 2 WARNING(S) FOUND IN THE INPUT INSTRUCTIONS SUMMARY OF ANALYSIS Number of groups 1 Number of observations 20 Number of dependent variables 1 Number of independent variables 0 Number of continuous latent variables 0 Observed dependent variables Continuous IQSCORE Estimator BAYES Specifications for Bayesian Estimation Point estimate MEDIAN Number of Markov chain Monte Carlo (MCMC) chains 2 Random seed for the first chain 0 Starting value information UNPERTURBED Treatment of categorical mediator LATENT Algorithm used for Markov chain Monte Carlo GIBBS(PX1) Convergence criterion 0.500D-01 Maximum number of iterations 50000 K-th iteration used for thinning 1 Input data file(s) data_IQ.dat Input data format FREE THE MODEL ESTIMATION TERMINATED NORMALLY USE THE FBITERATIONS OPTION TO INCREASE THE NUMBER OF ITERATIONS BY A FACTOR OF AT LEAST TWO TO CHECK CONVERGENCE AND THAT THE PSR VALUE DOES NOT INCREASE. MODEL FIT INFORMATION Number of Free Parameters 2 Bayesian Posterior Predictive Checking using Chi-Square 95% Confidence Interval for the Difference Between the Observed and the Replicated Chi-Square Values -4.724 4.093 Posterior Predictive P-Value 0.500 Information Criterion Deviance (DIC) 169.329 Estimated Number of Parameters (pD) 1.776 Bayesian (BIC) 171.623 MODEL RESULTS Posterior One-Tailed 95% C.I. Estimate S.D. P-Value Lower 2.5% Upper 2.5% Significance Means IQSCORE 101.952 3.881 0.000 95.007 111.027 * Variances IQSCORE 287.322 99.021 0.000 137.857 478.548 * STANDARDIZED MODEL RESULTS STDYX Standardization Posterior One-Tailed 95% C.I. Estimate S.D. P-Value Lower 2.5% Upper 2.5% Significance Means IQSCORE 6.031 1.029 0.000 4.543 8.556 * Variances IQSCORE 1.000 0.000 0.000 1.000 1.000 STDY Standardization Posterior One-Tailed 95% C.I. Estimate S.D. P-Value Lower 2.5% Upper 2.5% Significance Means IQSCORE 6.031 1.029 0.000 4.543 8.556 * Variances IQSCORE 1.000 0.000 0.000 1.000 1.000 STD Standardization Posterior One-Tailed 95% C.I. Estimate S.D. P-Value Lower 2.5% Upper 2.5% Significance Means IQSCORE 101.952 3.881 0.000 95.007 111.027 * Variances IQSCORE 287.322 99.021 0.000 137.857 478.548 * CREDIBILITY INTERVALS OF MODEL RESULTS Lower .5% Lower 2.5% Lower 5% Estimate Upper 5% Upper 2.5% Upper .5% Means IQSCORE 91.758 95.007 96.042 101.952 107.828 111.027 113.041 Variances IQSCORE 134.706 137.857 134.706 287.322 412.177 478.548 722.195 CREDIBILITY INTERVALS OF STANDARDIZED MODEL RESULTS STDYX Standardization Lower .5% Lower 2.5% Lower 5% Estimate Upper 5% Upper 2.5% Upper .5% Means IQSCORE 4.012 4.543 4.543 6.031 7.833 8.556 8.705 Variances IQSCORE 1.000 1.000 1.000 1.000 1.000 1.000 1.000 STDY Standardization Lower .5% Lower 2.5% Lower 5% Estimate Upper 5% Upper 2.5% Upper .5% Means IQSCORE 4.012 4.543 4.543 6.031 7.833 8.556 8.705 Variances IQSCORE 1.000 1.000 1.000 1.000 1.000 1.000 1.000 STD Standardization Lower .5% Lower 2.5% Lower 5% Estimate Upper 5% Upper 2.5% Upper .5% Means IQSCORE 91.758 95.007 96.042 101.952 107.828 111.027 113.041 Variances IQSCORE 134.706 137.857 134.706 287.322 412.177 478.548 722.195 TECHNICAL 1 OUTPUT PARAMETER SPECIFICATION NU IQSCORE ________ 1 1 THETA IQSCORE ________ IQSCORE 2 STARTING VALUES NU IQSCORE ________ 1 102.000 THETA IQSCORE ________ IQSCORE 118.421 PRIORS FOR ALL PARAMETERS PRIOR MEAN PRIOR VARIANCE PRIOR STD. DEV. Parameter 1~N(0.000,infinity) 0.0000 infinity infinity Parameter 2~IG(-1.000,0.000) infinity infinity infinity TECHNICAL 8 OUTPUT TECHNICAL 8 OUTPUT FOR BAYES ESTIMATION CHAIN BSEED 1 0 2 285380 POTENTIAL PARAMETER WITH ITERATION SCALE REDUCTION HIGHEST PSR 100 1.010 1 Kolmogorov-Smirnov comparing posterior distributions across chains 1 and 2 using 100 draws. Parameter KS Statistic P-value Parameter 1 0.2600 0.0560 Parameter 2 0.2000 0.2408 Simulated prior distributions Parameter Prior Mean Prior Variance Prior Std. Dev. Parameter 1 Improper Prior Parameter 2 Improper Prior PLOT INFORMATION The following plots are available: Histograms of sample values Scatterplots (sample values) Bayesian posterior parameter distributions Bayesian posterior parameter trace plots Bayesian autocorrelation plots Bayesian posterior predictive checking scatterplots Bayesian posterior predictive checking distribution plots Beginning Time: 16:43:21 Ending Time: 16:43:21 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) 1998-2012 Muthen & Muthen