Mplus VERSION 8.8
MUTHEN & MUTHEN
04/19/2022 11:11 PM
INPUT INSTRUCTIONS
TITLE: this is an example of a CFA with
continuous and categorical factor
indicators
DATA: FILE IS ex5.3.dat;
VARIABLE: NAMES ARE u1-u3 y4-y6;
CATEGORICAL ARE u1 u2 u3;
MODEL: f1 BY u1-u3;
f2 BY y4-y6;
INPUT READING TERMINATED NORMALLY
this is an example of a CFA with
continuous and categorical factor
indicators
SUMMARY OF ANALYSIS
Number of groups 1
Number of observations 500
Number of dependent variables 6
Number of independent variables 0
Number of continuous latent variables 2
Observed dependent variables
Continuous
Y4 Y5 Y6
Binary and ordered categorical (ordinal)
U1 U2 U3
Continuous latent variables
F1 F2
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)
ex5.3.dat
Input data format FREE
UNIVARIATE PROPORTIONS AND COUNTS FOR CATEGORICAL VARIABLES
U1
Category 1 0.504 252.000
Category 2 0.496 248.000
U2
Category 1 0.506 253.000
Category 2 0.494 247.000
U3
Category 1 0.496 248.000
Category 2 0.504 252.000
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
Y4 -0.016 -0.016 -3.215 0.20% -0.786 -0.287 -0.048
500.000 1.025 0.246 3.033 0.20% 0.212 0.795
Y5 -0.016 -0.103 -3.132 0.20% -0.955 -0.257 -0.033
500.000 1.026 -0.236 3.199 0.20% 0.257 0.903
Y6 0.014 0.036 -3.294 0.20% -0.859 -0.292 -0.042
500.000 0.988 -0.336 2.803 0.20% 0.285 0.921
THE MODEL ESTIMATION TERMINATED NORMALLY
MODEL FIT INFORMATION
Number of Free Parameters 16
Chi-Square Test of Model Fit
Value 3.935*
Degrees of Freedom 8
P-Value 0.8629
* 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 0.028
Probability RMSEA <= .05 0.995
CFI/TLI
CFI 1.000
TLI 1.000
Chi-Square Test of Model Fit for the Baseline Model
Value 1699.445
Degrees of Freedom 15
P-Value 0.0000
SRMR (Standardized Root Mean Square Residual)
Value 0.010
Optimum Function Value for Weighted Least-Squares Estimator
Value 0.94739409D-03
MODEL RESULTS
Two-Tailed
Estimate S.E. Est./S.E. P-Value
F1 BY
U1 1.000 0.000 999.000 999.000
U2 1.066 0.043 24.666 0.000
U3 1.000 0.039 25.935 0.000
F2 BY
Y4 1.000 0.000 999.000 999.000
Y5 1.051 0.039 26.737 0.000
Y6 0.997 0.041 24.331 0.000
F2 WITH
F1 -0.004 0.044 -0.094 0.925
Intercepts
Y4 -0.016 0.045 -0.350 0.726
Y5 -0.016 0.045 -0.358 0.720
Y6 0.014 0.044 0.312 0.755
Thresholds
U1$1 0.010 0.056 0.179 0.858
U2$1 0.015 0.056 0.268 0.788
U3$1 -0.010 0.056 -0.179 0.858
Variances
F1 0.801 0.046 17.468 0.000
F2 0.774 0.062 12.504 0.000
Residual Variances
Y4 0.251 0.025 10.219 0.000
Y5 0.170 0.021 7.950 0.000
Y6 0.218 0.021 10.507 0.000
QUALITY OF NUMERICAL RESULTS
Condition Number for the Information Matrix 0.393E-02
(ratio of smallest to largest eigenvalue)
R-SQUARE
Observed Residual
Variable Estimate Variance
U1 0.801 0.199
U2 0.911 0.089
U3 0.802 0.198
Y4 0.755
Y5 0.834
Y6 0.779
Beginning Time: 23:11:23
Ending Time: 23:11:24
Elapsed Time: 00:00:01
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