Mplus VERSION 8.8
MUTHEN & MUTHEN
04/19/2022 11:09 PM
INPUT INSTRUCTIONS
TITLE: this is an example of a probit regression
for a binary or categorical observed
dependent variable with two covariates
DATA: FILE IS ex3.4.dat;
VARIABLE: NAMES ARE u1 x1 x3;
CATEGORICAL = u1;
MODEL: u1 ON x1 x3;
INPUT READING TERMINATED NORMALLY
this is an example of a probit regression
for a binary or categorical observed
dependent variable with two covariates
SUMMARY OF ANALYSIS
Number of groups 1
Number of observations 500
Number of dependent variables 1
Number of independent variables 2
Number of continuous latent variables 0
Observed dependent variables
Binary and ordered categorical (ordinal)
U1
Observed independent variables
X1 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)
ex3.4.dat
Input data format FREE
UNIVARIATE PROPORTIONS AND COUNTS FOR CATEGORICAL VARIABLES
U1
Category 1 0.642 321.000
Category 2 0.358 179.000
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
X1 0.001 -0.133 -3.145 0.20% -0.922 -0.235 0.023
500.000 1.094 -0.162 2.920 0.20% 0.304 0.876
X3 -0.042 -0.057 -3.139 0.20% -0.921 -0.353 -0.040
500.000 0.957 -0.357 2.875 0.20% 0.274 0.859
THE MODEL ESTIMATION TERMINATED NORMALLY
MODEL FIT INFORMATION
Number of Free Parameters 3
Chi-Square Test of Model Fit
Value 0.000*
Degrees of Freedom 0
P-Value 0.0000
* 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 0.000
Probability RMSEA <= .05 0.000
CFI/TLI
CFI 1.000
TLI 1.000
Chi-Square Test of Model Fit for the Baseline Model
Value 193.243
Degrees of Freedom 2
P-Value 0.0000
SRMR (Standardized Root Mean Square Residual)
Value 0.000
Optimum Function Value for Weighted Least-Squares Estimator
Value 0.59857825D-08
MODEL RESULTS
Two-Tailed
Estimate S.E. Est./S.E. P-Value
U1 ON
X1 1.023 0.121 8.459 0.000
X3 2.474 0.224 11.029 0.000
Thresholds
U1$1 0.984 0.119 8.299 0.000
QUALITY OF NUMERICAL RESULTS
Condition Number for the Information Matrix 0.427E+00
(ratio of smallest to largest eigenvalue)
R-SQUARE
Observed Residual
Variable Estimate Variance
U1 0.877 1.000
Beginning Time: 23:09:22
Ending Time: 23:09:22
Elapsed Time: 00:00:00
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) 1998-2022 Muthen & Muthen
Back to examples