Mplus VERSION 8.7 (Mac) MUTHEN & MUTHEN 10/21/2021 10:45 PM INPUT INSTRUCTIONS TITLE: this is an example of moderated mediation with a plot of the indirect effect DATA: FILE = ex3.18.dat; VARIABLE: NAMES = y m x z; USEVARIABLES = y m x z xz; DEFINE: xz = x*z; ANALYSIS: ESTIMATOR = BAYES; PROCESSORS = 2; BITERATIONS = (30000); MODEL: y ON m (b) x z; m ON x (gamma1) z xz (gamma2); MODEL CONSTRAINT: PLOT(indirect); LOOP(mod,-2,2,0.1); indirect = b*(gamma1+gamma2*mod); PLOT: TYPE = PLOT2; OUTPUT: TECH8; INPUT READING TERMINATED NORMALLY this is an example of moderated mediation with a plot of the indirect effect SUMMARY OF ANALYSIS Number of groups 1 Number of observations 150 Number of dependent variables 2 Number of independent variables 3 Number of continuous latent variables 0 Observed dependent variables Continuous Y M Observed independent variables X Z XZ 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 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) ex3.18.dat Input data format FREE UNIVARIATE SAMPLE STATISTICS UNIVARIATE HIGHER-ORDER MOMENT DESCRIPTIVE STATISTICS Variable/ Mean/ Skewness/ Minimum/ % with Percentiles Sample Size Variance Kurtosis Maximum Min/Max 20%/60% 40%/80% Median Y -0.162 -0.068 -3.054 0.67% -0.808 -0.354 -0.213 150.000 0.709 0.769 2.199 0.67% 0.021 0.508 M -0.223 0.235 -2.459 0.67% -1.178 -0.510 -0.197 150.000 1.001 -0.244 2.528 0.67% 0.024 0.577 X 0.573 -0.297 0.000 42.67% 0.000 0.000 1.000 150.000 0.245 -1.912 1.000 57.33% 1.000 1.000 Z 0.085 0.139 -2.550 0.67% -0.698 -0.149 0.042 150.000 0.936 0.120 3.033 0.67% 0.290 0.917 XZ 0.073 0.570 -2.550 0.67% -0.308 0.000 0.000 150.000 0.549 3.074 3.033 0.67% 0.000 0.433 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 10 Bayesian Posterior Predictive Checking using Chi-Square 95% Confidence Interval for the Difference Between the Observed and the Replicated Chi-Square Values -7.445 18.769 Posterior Predictive P-Value 0.188 Information Criteria Deviance (DIC) 745.265 Estimated Number of Parameters (pD) 9.913 Bayesian (BIC) 775.413 RMSEA (Root Mean Square Error Of Approximation) Estimate 0.179 90 Percent C.I. 0.000 0.296 Probability RMSEA <= .05 0.083 CFI/TLI CFI 0.930 90 Percent C.I. 0.806 1.000 TLI 0.548 90 Percent C.I. 0.000 1.000 MODEL RESULTS Posterior One-Tailed 95% C.I. Estimate S.D. P-Value Lower 2.5% Upper 2.5% Significance Y ON M 0.518 0.057 0.000 0.406 0.630 * X 0.142 0.115 0.112 -0.085 0.369 Z 0.064 0.058 0.133 -0.050 0.180 M ON X -0.301 0.166 0.035 -0.629 0.023 Z 0.002 0.131 0.495 -0.259 0.259 XZ -0.177 0.172 0.149 -0.518 0.160 Intercepts Y -0.134 0.087 0.063 -0.304 0.037 M -0.037 0.125 0.384 -0.285 0.208 Residual Variances Y 0.471 0.057 0.000 0.376 0.600 * M 1.004 0.120 0.000 0.804 1.276 * TECHNICAL 1 OUTPUT PARAMETER SPECIFICATION NU Y M X Z XZ ________ ________ ________ ________ ________ 0 0 0 0 0 LAMBDA Y M X Z XZ ________ ________ ________ ________ ________ Y 0 0 0 0 0 M 0 0 0 0 0 X 0 0 0 0 0 Z 0 0 0 0 0 XZ 0 0 0 0 0 THETA Y M X Z XZ ________ ________ ________ ________ ________ Y 0 M 0 0 X 0 0 0 Z 0 0 0 0 XZ 0 0 0 0 0 ALPHA Y M X Z XZ ________ ________ ________ ________ ________ 1 2 0 0 0 BETA Y M X Z XZ ________ ________ ________ ________ ________ Y 0 3 4 5 0 M 0 0 6 7 8 X 0 0 0 0 0 Z 0 0 0 0 0 XZ 0 0 0 0 0 PSI Y M X Z XZ ________ ________ ________ ________ ________ Y 9 M 0 10 X 0 0 0 Z 0 0 0 0 XZ 0 0 0 0 0 PARAMETER SPECIFICATION FOR THE ADDITIONAL PARAMETERS NEW/ADDITIONAL PARAMETERS INDIRECT MOD ________ ________ 11 12 STARTING VALUES NU Y M X Z XZ ________ ________ ________ ________ ________ 0.000 0.000 0.000 0.000 0.000 LAMBDA Y M X Z XZ ________ ________ ________ ________ ________ Y 1.000 0.000 0.000 0.000 0.000 M 0.000 1.000 0.000 0.000 0.000 X 0.000 0.000 1.000 0.000 0.000 Z 0.000 0.000 0.000 1.000 0.000 XZ 0.000 0.000 0.000 0.000 1.000 THETA Y M X Z XZ ________ ________ ________ ________ ________ Y 0.000 M 0.000 0.000 X 0.000 0.000 0.000 Z 0.000 0.000 0.000 0.000 XZ 0.000 0.000 0.000 0.000 0.000 ALPHA Y M X Z XZ ________ ________ ________ ________ ________ -0.162 -0.223 0.000 0.000 0.000 BETA Y M X Z XZ ________ ________ ________ ________ ________ Y 0.000 0.000 0.000 0.000 0.000 M 0.000 0.000 0.000 0.000 0.000 X 0.000 0.000 0.000 0.000 0.000 Z 0.000 0.000 0.000 0.000 0.000 XZ 0.000 0.000 0.000 0.000 0.000 PSI Y M X Z XZ ________ ________ ________ ________ ________ Y 0.354 M 0.000 0.501 X 0.000 0.000 0.122 Z 0.000 0.000 0.000 0.468 XZ 0.000 0.000 0.000 0.000 0.275 STARTING VALUES FOR THE ADDITIONAL PARAMETERS NEW/ADDITIONAL PARAMETERS INDIRECT MOD ________ ________ 0.500 0.000 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~N(0.000,infinity) 0.0000 infinity infinity Parameter 7~N(0.000,infinity) 0.0000 infinity infinity Parameter 8~N(0.000,infinity) 0.0000 infinity infinity Parameter 9~IG(-1.000,0.000) infinity infinity infinity Parameter 10~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 1.046 7 200 1.014 8 300 1.012 8 400 1.002 4 500 1.003 5 600 1.001 7 700 1.003 8 800 1.002 4 900 1.001 8 1000 1.001 8 1100 1.002 4 1200 1.002 5 1300 1.004 5 1400 1.003 5 1500 1.003 5 1600 1.005 5 1700 1.003 5 1800 1.002 5 1900 1.003 5 2000 1.002 5 2100 1.002 5 2200 1.001 5 2300 1.002 5 2400 1.001 5 2500 1.001 5 2600 1.002 3 2700 1.002 5 2800 1.001 5 2900 1.001 9 3000 1.001 9 3100 1.001 9 3200 1.001 9 3300 1.001 9 3400 1.001 9 3500 1.001 9 3600 1.001 1 3700 1.001 1 3800 1.000 4 3900 1.000 1 4000 1.000 4 4100 1.000 4 4200 1.001 4 4300 1.000 4 4400 1.000 1 4500 1.001 1 4600 1.001 1 4700 1.001 1 4800 1.000 1 4900 1.000 10 5000 1.001 10 5100 1.000 10 5200 1.000 10 5300 1.000 10 5400 1.000 10 5500 1.001 10 5600 1.001 10 5700 1.000 10 5800 1.000 8 5900 1.000 10 6000 1.000 10 6100 1.000 10 6200 1.000 10 6300 1.000 10 6400 1.000 10 6500 1.000 10 6600 1.000 10 6700 1.000 8 6800 1.000 10 6900 1.000 10 7000 1.000 10 7100 1.000 8 7200 1.000 6 7300 1.000 8 7400 1.000 8 7500 1.000 8 7600 1.000 8 7700 1.000 8 7800 1.000 8 7900 1.000 8 8000 1.000 3 8100 1.000 3 8200 1.000 3 8300 1.000 3 8400 1.000 3 8500 1.000 3 8600 1.000 3 8700 1.000 3 8800 1.000 3 8900 1.000 3 9000 1.000 3 9100 1.000 3 9200 1.000 3 9300 1.000 3 9400 1.000 3 9500 1.000 3 9600 1.000 3 9700 1.000 1 9800 1.000 1 9900 1.000 1 10000 1.000 3 10100 1.000 1 10200 1.000 3 10300 1.000 10 10400 1.000 1 10500 1.000 1 10600 1.000 1 10700 1.000 1 10800 1.000 5 10900 1.000 10 11000 1.000 5 11100 1.000 5 11200 1.000 5 11300 1.000 5 11400 1.000 10 11500 1.000 10 11600 1.000 10 11700 1.000 10 11800 1.000 10 11900 1.000 10 12000 1.000 10 12100 1.000 10 12200 1.000 10 12300 1.000 10 12400 1.000 10 12500 1.000 10 12600 1.000 10 12700 1.000 10 12800 1.000 10 12900 1.000 10 13000 1.000 10 13100 1.000 10 13200 1.000 10 13300 1.000 10 13400 1.000 10 13500 1.000 1 13600 1.000 1 13700 1.000 1 13800 1.000 1 13900 1.000 10 14000 1.000 10 14100 1.000 10 14200 1.000 10 14300 1.000 10 14400 1.000 10 14500 1.000 10 14600 1.000 10 14700 1.000 4 14800 1.000 1 14900 1.000 1 15000 1.000 1 15100 1.000 1 15200 1.000 1 15300 1.000 1 15400 1.000 1 15500 1.000 10 15600 1.000 3 15700 1.000 3 15800 1.000 3 15900 1.000 3 16000 1.000 3 16100 1.000 3 16200 1.000 3 16300 1.000 3 16400 1.000 3 16500 1.000 3 16600 1.000 10 16700 1.000 10 16800 1.000 10 16900 1.000 10 17000 1.000 10 17100 1.000 10 17200 1.000 10 17300 1.000 10 17400 1.000 10 17500 1.000 3 17600 1.000 10 17700 1.000 3 17800 1.000 3 17900 1.000 3 18000 1.000 3 18100 1.000 3 18200 1.000 3 18300 1.000 3 18400 1.000 3 18500 1.000 3 18600 1.000 3 18700 1.000 3 18800 1.000 3 18900 1.000 3 19000 1.000 3 19100 1.000 3 19200 1.000 3 19300 1.000 3 19400 1.000 1 19500 1.000 1 19600 1.000 1 19700 1.000 1 19800 1.000 1 19900 1.000 1 20000 1.000 1 20100 1.000 1 20200 1.000 1 20300 1.000 1 20400 1.000 1 20500 1.000 1 20600 1.000 1 20700 1.000 1 20800 1.000 1 20900 1.000 1 21000 1.000 1 21100 1.000 1 21200 1.000 1 21300 1.000 1 21400 1.000 1 21500 1.000 1 21600 1.000 1 21700 1.000 1 21800 1.000 1 21900 1.000 1 22000 1.000 1 22100 1.000 1 22200 1.000 1 22300 1.000 1 22400 1.000 1 22500 1.000 1 22600 1.000 1 22700 1.000 1 22800 1.000 1 22900 1.000 8 23000 1.000 8 23100 1.000 1 23200 1.000 8 23300 1.000 8 23400 1.000 8 23500 1.000 8 23600 1.000 8 23700 1.000 9 23800 1.000 9 23900 1.000 9 24000 1.000 9 24100 1.000 9 24200 1.000 7 24300 1.000 7 24400 1.000 7 24500 1.000 9 24600 1.000 7 24700 1.000 8 24800 1.000 8 24900 1.000 9 25000 1.000 2 25100 1.000 8 25200 1.000 8 25300 1.000 2 25400 1.000 9 25500 1.000 7 25600 1.000 7 25700 1.000 8 25800 1.000 8 25900 1.000 8 26000 1.000 7 26100 1.000 2 26200 1.000 2 26300 1.000 7 26400 1.000 7 26500 1.000 7 26600 1.000 7 26700 1.000 7 26800 1.000 7 26900 1.000 7 27000 1.000 2 27100 1.000 2 27200 1.000 2 27300 1.000 2 27400 1.000 2 27500 1.000 2 27600 1.000 2 27700 1.000 2 27800 1.000 2 27900 1.000 2 28000 1.000 2 28100 1.000 2 28200 1.000 2 28300 1.000 2 28400 1.000 2 28500 1.000 2 28600 1.000 2 28700 1.000 2 28800 1.000 2 28900 1.000 2 29000 1.000 2 29100 1.000 2 29200 1.000 7 29300 1.000 7 29400 1.000 7 29500 1.000 7 29600 1.000 2 29700 1.000 2 29800 1.000 2 29900 1.000 2 30000 1.000 2 PLOT INFORMATION The following plots are available: Loop plots Bayesian posterior parameter distributions Bayesian posterior parameter trace plots Bayesian autocorrelation plots Bayesian prior parameter distributions Bayesian posterior predictive checking scatterplots Bayesian posterior predictive checking distribution plots Beginning Time: 22:45:55 Ending Time: 22:45:56 Elapsed Time: 00:00:01 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) 1998-2021 Muthen & Muthen