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 combination of continuous and
categorical dependent variables
DATA: FILE IS ex3.14.dat;
VARIABLE: NAMES ARE y1 y2 u1 x1-x3;
CATEGORICAL IS u1;
MODEL: y1 y2 ON x1 x2 x3;
u1 ON y1 y2 x2;
INPUT READING TERMINATED NORMALLY
this is an example of a path analysis
with a combination of continuous and
categorical dependent variables
SUMMARY OF ANALYSIS
Number of groups 1
Number of observations 500
Number of dependent variables 3
Number of independent variables 3
Number of continuous latent variables 0
Observed dependent variables
Continuous
Y1 Y2
Binary and ordered categorical (ordinal)
U1
Observed independent variables
X1 X2 X3
Estimator WLSMV
Maximum number of iterations 1000
Convergence criterion 0.500D-04
Maximum number of steepest descent iterations 20
Parameterization DELTA
Link PROBIT
Input data file(s)
ex3.14.dat
Input data format FREE
UNIVARIATE PROPORTIONS AND COUNTS FOR CATEGORICAL VARIABLES
U1
Category 1 0.664 332.000
Category 2 0.336 168.000
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
Y1 0.950 0.007 -1.278 0.20% 0.160 0.742 0.984
500.000 0.716 -0.427 3.170 0.20% 1.178 1.722
Y2 1.987 0.050 0.186 0.20% 1.432 1.803 2.001
500.000 0.424 -0.114 4.374 0.20% 2.141 2.559
X1 0.046 0.006 -3.268 0.20% -0.875 -0.207 0.030
500.000 1.143 0.311 3.468 0.20% 0.358 0.873
X2 -0.027 -0.152 -2.818 0.20% -0.986 -0.221 0.093
500.000 1.066 -0.277 2.993 0.20% 0.341 0.820
X3 -0.012 0.034 -3.229 0.20% -0.798 -0.270 -0.038
500.000 1.074 0.285 3.252 0.20% 0.219 0.851
THE MODEL ESTIMATION TERMINATED NORMALLY
MODEL FIT INFORMATION
Number of Free Parameters 14
Chi-Square Test of Model Fit
Value 1.578*
Degrees of Freedom 3
P-Value 0.6644
* The chi-square value for MLM, MLMV, MLR, ULSMV, WLSM and WLSMV cannot be used
for chi-square difference testing in the regular way. MLM, MLR and WLSM
chi-square difference testing is described on the Mplus website. MLMV, WLSMV,
and ULSMV difference testing is done using the DIFFTEST option.
RMSEA (Root Mean Square Error Of Approximation)
Estimate 0.000
90 Percent C.I. 0.000 0.059
Probability RMSEA <= .05 0.915
CFI/TLI
CFI 1.000
TLI 1.000
Chi-Square Test of Model Fit for the Baseline Model
Value 670.933
Degrees of Freedom 12
P-Value 0.0000
SRMR (Standardized Root Mean Square Residual)
Value 0.010
Optimum Function Value for Weighted Least-Squares Estimator
Value 0.11912860D-02
MODEL RESULTS
Two-Tailed
Estimate S.E. Est./S.E. P-Value
Y1 ON
X1 0.088 0.031 2.861 0.004
X2 0.201 0.032 6.271 0.000
X3 0.347 0.036 9.542 0.000
Y2 ON
X1 0.275 0.023 12.053 0.000
X2 0.196 0.024 8.242 0.000
X3 0.105 0.024 4.369 0.000
U1 ON
Y1 1.013 0.060 16.994 0.000
Y2 -1.061 0.103 -10.356 0.000
X2 2.057 0.192 10.710 0.000
Intercepts
Y1 0.955 0.033 29.106 0.000
Y2 1.981 0.024 82.469 0.000
Thresholds
U1$1 -0.078 0.243 -0.322 0.748
Residual Variances
Y1 0.526 0.035 14.881 0.000
Y2 0.280 0.019 14.400 0.000
QUALITY OF NUMERICAL RESULTS
Condition Number for the Information Matrix 0.262E-02
(ratio of smallest to largest eigenvalue)
R-SQUARE
Observed Residual
Variable Estimate Variance
Y1 0.268
Y2 0.341
U1 0.974 0.145
Beginning Time: 23:09:15
Ending Time: 23:09:15
Elapsed Time: 00:00:00
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