Mplus VERSION 8.7 (Mac) MUTHEN & MUTHEN 10/21/2021 10:45 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: 22:45:56 Ending Time: 22:45:56 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) 1998-2021 Muthen & Muthen