Mplus VERSION 7.4
MUTHEN & MUTHEN
10/20/2015   5:41 PM

INPUT INSTRUCTIONS

  TITLE:
  	path analysis with a binary outcome and a continuous
  	mediator with missing: Monte Carlo integration
  DATA:
  	FILE = lsaydropout.dat;
  VARIABLE:
      NAMES = lsayid female mothed homeres expect math7 math8
  	math9 math10 lunch expel arrest droptht7 hisp black hsdrop
  	md710 urban tracking ntrack mothsei race mth11 mth12
  	totstud lchpart african hispan asian self worth other
  	satisf respect failure esteem problem cloctn dloctn eloctn
  	floctn gloctn hloctn iloctn jloctn kloctn lloctn drop7
  	drop8 drop9 drop10 drop11 drop12 cloc dloc eloc floc
  	gloc hloc iloc jloc kloc lloc dopout misdrop miss7
  	miss8 miss9 miss10 pat1 pat2 pat3 pat4 pat5 pat6 pat7
  	pat8 pat9 pat10 pat11 pat12 pat13 pat14 pat15 pat16 drop
  	dof12 dos11 dof11 dos10 dof10 dos9 dof9 dos8 dof8 cprob1
  	cprob2 cprob3 class expect7 expect8 expect9 expect10
  	expect11 expect12 dropot7 dropot8 dropot9 dropot10
  	dropot11 dropot12;
     !pat1-16== missing data pattern dummy
     !lloc2==dropout status at 12th grade (0=not dropout, 1=dropout)
     !expect7-12==college expectations in 7-12th grade
     !dropt7-12==thoughts of dropout in 7-12th grade
  USEV = math7 math10;
  IDVARIABLE = lsayid;
  MISSING = ALL(9999);
  USEOBS = (hloc == 0 OR iloc == 0 OR jloc == 0 OR kloc == 0
               OR lloc == 0) AND math7 NE 9999;

  ANALYSIS:
  	ESTIMATOR = bayes;
      processors = 2;
      biter = (10000);
  MODEL:
  	math7 WITH math10;

  OUTPUT:
  	patterns standardized tech1 tech8;

  plot:
      type = plot2;




INPUT READING TERMINATED NORMALLY




path analysis with a binary outcome and a continuous
mediator with missing: Monte Carlo integration

SUMMARY OF ANALYSIS

Number of groups                                                 1
Number of observations                                        2898

Number of dependent variables                                    2
Number of independent variables                                  0
Number of continuous latent variables                            0

Observed dependent variables

  Continuous
   MATH7       MATH10

Variables with special functions

  ID variable           LSAYID

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)
  lsaydropout.dat
Input data format  FREE


SUMMARY OF DATA



SUMMARY OF MISSING DATA PATTERNS

     Number of missing data patterns             2


     MISSING DATA PATTERNS (x = not missing)

           1  2
 MATH7     x  x
 MATH10    x


     MISSING DATA PATTERN FREQUENCIES

    Pattern   Frequency     Pattern   Frequency
          1        2009           2         889


COVARIANCE COVERAGE OF DATA

Minimum covariance coverage value   0.100


     PROPORTION OF DATA PRESENT


           Covariance Coverage
              MATH7         MATH10
              ________      ________
 MATH7          1.000
 MATH10         0.693         0.693



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                               5

Bayesian Posterior Predictive Checking using Chi-Square

          95% Confidence Interval for the Difference Between
          the Observed and the Replicated Chi-Square Values

                                 -9.677             9.470

          Posterior Predictive P-Value              0.467

Information Criteria

          Deviance (DIC)                        35910.259
          Estimated Number of Parameters (pD)       5.166
          Bayesian (BIC)                        35939.781



MODEL RESULTS

                                Posterior  One-Tailed         95% C.I.
                    Estimate       S.D.      P-Value   Lower 2.5%  Upper 2.5%  Significance

 MATH7    WITH
    MATH10           108.429       3.461      0.000     101.868     115.376      *

 Means
    MATH7             50.884       0.189      0.000      50.518      51.253      *
    MATH10            62.996       0.273      0.000      62.461      63.542      *

 Variances
    MATH7            102.864       2.720      0.000      97.694     108.355      *
    MATH10           185.136       5.514      0.000     174.735     196.362      *


STANDARDIZED MODEL RESULTS


STDYX Standardization

                                Posterior  One-Tailed         95% C.I.
                    Estimate       S.D.      P-Value   Lower 2.5%  Upper 2.5%  Significance
 MATH7    WITH
    MATH10             0.786       0.008      0.000       0.769       0.801      *

 Means
    MATH7              5.017       0.069      0.000       4.884       5.154      *
    MATH10             4.630       0.073      0.000       4.489       4.771      *

 Variances
    MATH7              1.000       0.000      0.000       1.000       1.000
    MATH10             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
 MATH7    WITH
    MATH10             0.786       0.008      0.000       0.769       0.801      *

 Means
    MATH7              5.017       0.069      0.000       4.884       5.154      *
    MATH10             4.630       0.073      0.000       4.489       4.771      *

 Variances
    MATH7              1.000       0.000      0.000       1.000       1.000
    MATH10             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
 MATH7    WITH
    MATH10             0.786       0.008      0.000       0.769       0.801      *

 Means
    MATH7             50.884       0.189      0.000      50.518      51.253      *
    MATH10            62.996       0.273      0.000      62.461      63.542      *

 Variances
    MATH7            102.864       2.720      0.000      97.694     108.355      *
    MATH10           185.136       5.514      0.000     174.735     196.362      *


TECHNICAL 1 OUTPUT


     PARAMETER SPECIFICATION


           NU
              MATH7         MATH10
              ________      ________
 1                  1             2


           THETA
              MATH7         MATH10
              ________      ________
 MATH7              3
 MATH10             4             5


     STARTING VALUES


           NU
              MATH7         MATH10
              ________      ________
 1             50.882        63.713


           THETA
              MATH7         MATH10
              ________      ________
 MATH7         51.330
 MATH10         0.000        92.492



     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~IW(0.000,-3)              infinity            infinity            infinity
     Parameter 4~IW(0.000,-3)              infinity            infinity            infinity
     Parameter 5~IW(0.000,-3)              infinity            infinity            infinity


TECHNICAL 8 OUTPUT



     Kolmogorov-Smirnov comparing posterior distributions across chains 1 and 2 using 100 draws.





     Parameter   KS Statistic P-value
     Parameter 1    0.1600    0.1400
     Parameter 2    0.1100    0.5560
     Parameter 3    0.1100    0.5560
     Parameter 5    0.0700    0.9610
     Parameter 4    0.0600    0.9921



     Simulated prior distributions

     Parameter       Prior Mean  Prior Variance  Prior Std. Dev.


     Parameter 1 Improper Prior
     Parameter 2 Improper Prior
     Parameter 3 Improper Prior
     Parameter 4 Improper Prior
     Parameter 5 Improper Prior


   TECHNICAL 8 OUTPUT FOR BAYES ESTIMATION

     CHAIN    BSEED
     1        0
     2        285380

                     POTENTIAL       PARAMETER WITH
     ITERATION    SCALE REDUCTION      HIGHEST PSR
     100              1.003               2
     200              1.034               2
     300              1.005               2
     400              1.000               1
     500              1.001               3
     600              1.003               3
     700              1.003               3
     800              1.001               5
     900              1.001               2
     1000             1.000               2
     1100             1.000               1
     1200             1.001               1
     1300             1.002               3
     1400             1.001               5
     1500             1.001               5
     1600             1.000               1
     1700             1.003               5
     1800             1.003               5
     1900             1.003               5
     2000             1.001               5
     2100             1.000               1
     2200             1.001               5
     2300             1.001               5
     2400             1.001               5
     2500             1.000               5
     2600             1.000               5
     2700             1.000               1
     2800             1.000               1
     2900             1.000               4
     3000             1.000               2
     3100             1.000               2
     3200             1.000               2
     3300             1.000               2
     3400             1.000               3
     3500             1.000               3
     3600             1.000               3
     3700             1.000               3
     3800             1.000               2
     3900             1.001               2
     4000             1.001               2
     4100             1.001               2
     4200             1.001               2
     4300             1.001               2
     4400             1.001               2
     4500             1.001               2
     4600             1.000               2
     4700             1.000               2
     4800             1.000               2
     4900             1.001               2
     5000             1.000               2
     5100             1.000               4
     5200             1.000               4
     5300             1.000               4
     5400             1.000               4
     5500             1.000               4
     5600             1.001               4
     5700             1.001               4
     5800             1.001               4
     5900             1.000               4
     6000             1.000               4
     6100             1.001               4
     6200             1.001               4
     6300             1.001               4
     6400             1.001               4
     6500             1.001               4
     6600             1.001               4
     6700             1.001               4
     6800             1.001               4
     6900             1.001               4
     7000             1.001               4
     7100             1.001               4
     7200             1.001               4
     7300             1.001               4
     7400             1.001               4
     7500             1.001               4
     7600             1.001               4
     7700             1.001               4
     7800             1.001               4
     7900             1.001               4
     8000             1.001               4
     8100             1.001               4
     8200             1.001               4
     8300             1.001               4
     8400             1.001               4
     8500             1.001               4
     8600             1.001               4
     8700             1.001               4
     8800             1.001               4
     8900             1.001               4
     9000             1.001               4
     9100             1.001               4
     9200             1.001               4
     9300             1.001               4
     9400             1.001               5
     9500             1.001               5
     9600             1.001               5
     9700             1.001               5
     9800             1.001               5
     9900             1.001               5
     10000            1.001               4


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

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\bengt 2013\documents\bengt\mplus runs\a book - topic 1 mplus runs\bayes\bivar lsay\math

     Beginning Time:  17:41:23
        Ending Time:  17:41:27
       Elapsed Time:  00:00:04



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