Mplus VERSION 6.1 MUTHEN & MUTHEN 10/15/2010 12:54 PM INPUT INSTRUCTIONS title: Yuan and MacKinnon firefighters mediation using Bayesian analysis Elliot DL, Goldberg L, Kuehl KS, et al. The PHLAME Study: process and outcomes of 2 models of behavior change. J Occup Environ Med. 2007;49(2):204-213. data: file = fire.dat; variable: names = y m x; model: m on x (a); y on m (b) x; analysis: estimator = bayes; process = 2; fbiter = 10000; model constraint: new(indirect); indirect = a*b; output: tech1 tech8; plot: type = plot2; INPUT READING TERMINATED NORMALLY Yuan and MacKinnon firefighters mediation using Bayesian analysis Elliot DL, Goldberg L, Kuehl KS, et al. The PHLAME Study: process and outcomes of 2 models of behavior change. J Occup Environ Med. 2007;49(2):204-213. SUMMARY OF ANALYSIS Number of groups 1 Number of observations 354 Number of dependent variables 2 Number of independent variables 1 Number of continuous latent variables 0 Observed dependent variables Continuous Y M Observed independent variables X 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) Fixed number of iterations 10000 K-th iteration used for thinning 1 Input data file(s) fire.dat Input data format FREE THE MODEL ESTIMATION TERMINATED NORMALLY MODEL FIT INFORMATION Number of Free Parameters 7 Bayesian Posterior Predictive Checking using Chi-Square 95% Confidence Interval for the Difference Between the Observed and the Replicated Chi-Square Values -10.750 10.828 Posterior Predictive P-Value 0.487 Information Criterion Deviance (DIC) 2130.170 Estimated Number of Parameters (pD) 6.959 MODEL RESULTS Posterior One-Tailed 95% C.I. Estimate S.D. P-Value Lower 2.5% Upper 2.5% M ON X 0.395 0.121 0.000 0.160 0.634 Y ON M 0.142 0.052 0.003 0.040 0.243 X 0.108 0.117 0.176 -0.127 0.339 Intercepts Y 0.418 0.057 0.000 0.308 0.530 M 0.000 0.059 0.499 -0.115 0.116 Residual Variances Y 1.144 0.089 0.000 0.987 1.338 M 1.218 0.093 0.000 1.054 1.419 New/Additional Parameters INDIRECT 0.053 0.028 0.004 0.011 0.117 TECHNICAL 1 OUTPUT PARAMETER SPECIFICATION NU Y M X ________ ________ ________ 1 0 0 0 LAMBDA Y M X ________ ________ ________ Y 0 0 0 M 0 0 0 X 0 0 0 THETA Y M X ________ ________ ________ Y 0 M 0 0 X 0 0 0 ALPHA Y M X ________ ________ ________ 1 1 2 0 BETA Y M X ________ ________ ________ Y 0 3 4 M 0 0 5 X 0 0 0 PSI Y M X ________ ________ ________ Y 6 M 0 7 X 0 0 0 PARAMETER SPECIFICATION FOR THE ADDITIONAL PARAMETERS NEW/ADDITIONAL PARAMETERS INDIRECT ________ 1 8 STARTING VALUES NU Y M X ________ ________ ________ 1 0.000 0.000 0.000 LAMBDA Y M X ________ ________ ________ Y 1.000 0.000 0.000 M 0.000 1.000 0.000 X 0.000 0.000 1.000 THETA Y M X ________ ________ ________ Y 0.000 M 0.000 0.000 X 0.000 0.000 0.000 ALPHA Y M X ________ ________ ________ 1 0.418 0.000 0.000 BETA Y M X ________ ________ ________ Y 0.000 0.000 0.000 M 0.000 0.000 0.000 X 0.000 0.000 0.000 PSI Y M X ________ ________ ________ Y 0.579 M 0.000 0.622 X 0.000 0.000 0.121 STARTING VALUES FOR THE ADDITIONAL PARAMETERS NEW/ADDITIONAL PARAMETERS INDIRECT ________ 1 0.500 PRIORS FOR ALL PARAMETERS PRIOR MEAN PRIOR VARIANCE PRIOR STD. DEV. Parameter 1~N(0.000,infinity) 0.0000 infinity infinity Parameter 2~N(0.000,infinity) 0.0000 infinity infinity Parameter 3~N(0.000,infinity) 0.0000 infinity infinity Parameter 4~N(0.000,infinity) 0.0000 infinity infinity Parameter 5~N(0.000,infinity) 0.0000 infinity infinity Parameter 6~IG(-1.000,0.000) infinity infinity infinity Parameter 7~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 1.030 4 200 1.012 1 300 1.001 4 400 1.008 3 500 1.004 3 600 1.002 6 700 1.000 3 800 1.003 1 900 1.002 1 1000 1.002 1 1100 1.002 2 1200 1.003 2 1300 1.003 2 1400 1.003 1 1500 1.002 2 1600 1.002 6 1700 1.001 2 1800 1.001 6 1900 1.002 6 2000 1.002 6 2100 1.003 6 2200 1.003 6 2300 1.002 6 2400 1.002 6 2500 1.001 6 2600 1.001 5 2700 1.001 5 2800 1.000 5 2900 1.000 1 3000 1.000 1 3100 1.001 1 3200 1.001 1 3300 1.002 1 3400 1.001 1 3500 1.000 1 3600 1.001 1 3700 1.001 1 3800 1.000 1 3900 1.000 1 4000 1.000 1 4100 1.000 1 4200 1.000 1 4300 1.000 1 4400 1.000 1 4500 1.000 1 4600 1.000 1 4700 1.000 1 4800 1.001 2 4900 1.001 2 5000 1.001 4 5100 1.001 4 5200 1.001 1 5300 1.001 1 5400 1.001 4 5500 1.001 4 5600 1.001 4 5700 1.001 4 5800 1.001 4 5900 1.001 4 6000 1.001 4 6100 1.000 2 6200 1.000 1 6300 1.000 1 6400 1.000 1 6500 1.001 1 6600 1.000 1 6700 1.000 1 6800 1.000 1 6900 1.000 1 7000 1.000 1 7100 1.000 1 7200 1.000 1 7300 1.000 1 7400 1.000 1 7500 1.000 1 7600 1.000 1 7700 1.000 1 7800 1.000 1 7900 1.000 4 8000 1.000 4 8100 1.000 4 8200 1.000 4 8300 1.000 4 8400 1.000 2 8500 1.000 1 8600 1.000 2 8700 1.000 1 8800 1.000 1 8900 1.000 1 9000 1.000 1 9100 1.000 1 9200 1.000 1 9300 1.000 1 9400 1.000 1 9500 1.000 1 9600 1.000 1 9700 1.000 1 9800 1.000 1 9900 1.000 1 10000 1.000 1 PLOT INFORMATION The following plots are available: 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: 12:54:11 Ending Time: 12:54:11 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