Mplus VERSION 8.8
MUTHEN & MUTHEN
04/19/2022 11:09 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: 23:09:19
Ending Time: 23:09:20
Elapsed Time: 00:00:01
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