Mplus VERSION 8.8
MUTHEN & MUTHEN
04/19/2022 11:09 PM
INPUT INSTRUCTIONS
TITLE: this is an example of a path analysis
with a categorical dependent variable and
a continuous mediating variable with
missing data
DATA: FILE IS ex3.17.dat;
VARIABLE: NAMES ARE u y x;
CATEGORICAL IS u;
MISSING IS y (999);
ANALYSIS:ESTIMATOR = MLR;
INTEGRATION = MONTECARLO;
MODEL: y ON x;
u ON y x;
OUTPUT: TECH1 TECH8;
INPUT READING TERMINATED NORMALLY
this is an example of a path analysis
with a categorical dependent variable and
a continuous mediating variable with
missing data
SUMMARY OF ANALYSIS
Number of groups 1
Number of observations 500
Number of dependent variables 2
Number of independent variables 1
Number of continuous latent variables 0
Observed dependent variables
Continuous
Y
Binary and ordered categorical (ordinal)
U
Observed independent variables
X
Estimator MLR
Information matrix OBSERVED
Optimization Specifications for the Quasi-Newton Algorithm for
Continuous Outcomes
Maximum number of iterations 100
Convergence criterion 0.100D-05
Optimization Specifications for the EM Algorithm
Maximum number of iterations 500
Convergence criteria
Loglikelihood change 0.100D-02
Relative loglikelihood change 0.100D-05
Derivative 0.100D-02
Optimization Specifications for the M step of the EM Algorithm for
Categorical Latent variables
Number of M step iterations 1
M step convergence criterion 0.100D-02
Basis for M step termination ITERATION
Optimization Specifications for the M step of the EM Algorithm for
Censored, Binary or Ordered Categorical (Ordinal), Unordered
Categorical (Nominal) and Count Outcomes
Number of M step iterations 1
M step convergence criterion 0.100D-02
Basis for M step termination ITERATION
Maximum value for logit thresholds 15
Minimum value for logit thresholds -15
Minimum expected cell size for chi-square 0.100D-01
Maximum number of iterations for H1 2000
Convergence criterion for H1 0.100D-03
Optimization algorithm EMA
Integration Specifications
Type MONTECARLO
Number of integration points 250
Dimensions of numerical integration 1
Adaptive quadrature ON
Monte Carlo integration seed 0
Link LOGIT
Cholesky OFF
Input data file(s)
ex3.17.dat
Input data format FREE
SUMMARY OF DATA
Number of missing data patterns 2
Number of y missing data patterns 2
Number of u missing data patterns 1
COVARIANCE COVERAGE OF DATA
Minimum covariance coverage value 0.100
PROPORTION OF DATA PRESENT FOR Y
Covariance Coverage
Y X
________ ________
Y 0.698
X 0.698 1.000
UNIVARIATE PROPORTIONS AND COUNTS FOR CATEGORICAL VARIABLES
U
Category 1 0.470 235.000
Category 2 0.530 265.000
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 0.820 0.072 -3.129 0.29% -0.374 0.459 0.826
349.000 1.930 0.111 5.091 0.29% 1.106 1.956
X -0.067 -0.060 -3.145 0.20% -0.870 -0.304 -0.034
500.000 0.960 0.073 2.857 0.20% 0.205 0.741
THE MODEL ESTIMATION TERMINATED NORMALLY
MODEL FIT INFORMATION
Number of Free Parameters 6
Loglikelihood
H0 Value -820.920
H0 Scaling Correction Factor 1.0277
for MLR
Information Criteria
Akaike (AIC) 1653.839
Bayesian (BIC) 1679.127
Sample-Size Adjusted BIC 1660.083
(n* = (n + 2) / 24)
MODEL RESULTS
Two-Tailed
Estimate S.E. Est./S.E. P-Value
Y ON
X -1.006 0.060 -16.894 0.000
U ON
Y 0.489 0.123 3.974 0.000
X 1.027 0.171 5.993 0.000
Intercepts
Y 0.537 0.056 9.677 0.000
Thresholds
U$1 0.093 0.117 0.796 0.426
Residual Variances
Y 1.019 0.077 13.234 0.000
QUALITY OF NUMERICAL RESULTS
Condition Number for the Information Matrix 0.492E-01
(ratio of smallest to largest eigenvalue)
LOGISTIC REGRESSION ODDS RATIO RESULTS
95% C.I.
Estimate S.E. Lower 2.5% Upper 2.5%
U ON
Y 1.630 0.200 1.281 2.074
X 2.792 0.478 1.996 3.907
TECHNICAL 1 OUTPUT
PARAMETER SPECIFICATION
TAU
U$1
________
6
NU
U Y X
________ ________ ________
0 0 0
LAMBDA
U Y X
________ ________ ________
U 0 0 0
Y 0 0 0
X 0 0 0
THETA
U Y X
________ ________ ________
U 0
Y 0 0
X 0 0 0
ALPHA
U Y X
________ ________ ________
0 1 0
BETA
U Y X
________ ________ ________
U 0 2 3
Y 0 0 4
X 0 0 0
PSI
U Y X
________ ________ ________
U 0
Y 0 5
X 0 0 0
STARTING VALUES
TAU
U$1
________
-0.120
NU
U Y X
________ ________ ________
0.000 0.000 0.000
LAMBDA
U Y X
________ ________ ________
U 1.000 0.000 0.000
Y 0.000 1.000 0.000
X 0.000 0.000 1.000
THETA
U Y X
________ ________ ________
U 0.000
Y 0.000 0.000
X 0.000 0.000 0.000
ALPHA
U Y X
________ ________ ________
0.000 0.820 0.000
BETA
U Y X
________ ________ ________
U 0.000 0.000 0.000
Y 0.000 0.000 0.000
X 0.000 0.000 0.000
PSI
U Y X
________ ________ ________
U 1.000
Y 0.000 0.965
X 0.000 0.000 0.480
TECHNICAL 8 OUTPUT
E STEP ITER LOGLIKELIHOOD ABS CHANGE REL CHANGE ALGORITHM
1 -0.10091903D+04 0.0000000 0.0000000 EM
2 -0.84555613D+03 163.6341494 0.1621440 EM
3 -0.82681722D+03 18.7389086 0.0221616 EM
4 -0.82226690D+03 4.5503226 0.0055034 EM
5 -0.82123578D+03 1.0311165 0.0012540 EM
6 -0.82100270D+03 0.2330777 0.0002838 EM
7 -0.82094508D+03 0.0576210 0.0000702 EM
8 -0.82092860D+03 0.0164822 0.0000201 EM
9 -0.82092306D+03 0.0055363 0.0000067 EM
10 -0.82092094D+03 0.0021243 0.0000026 EM
11 -0.82092005D+03 0.0008895 0.0000011 EM
12 -0.82091966D+03 0.0003909 0.0000005 EM
Beginning Time: 23:09:16
Ending Time: 23:09:18
Elapsed Time: 00:00:02
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