Mplus VERSION 8.8
MUTHEN & MUTHEN
04/19/2022 11:11 PM
INPUT INSTRUCTIONS
TITLE: this is an example of a threshold
structure CFA for categorical factor indicators
DATA: FILE IS ex5.10.dat;
VARIABLE: NAMES ARE u1a-u1c u2a-u2c;
CATEGORICAL ARE u1a-u1c u2a-u2c;
MODEL: f1 BY u1a u1b@1 u1c@1;
f2 BY u2a u2b@1 u2c@1;
[u1a$1 u1b$1 u1c$1] (1);
[u2a$1 u2b$1 u2c$1] (2);
INPUT READING TERMINATED NORMALLY
this is an example of a threshold
structure CFA for 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
Binary and ordered categorical (ordinal)
U1A U1B U1C U2A U2B U2C
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)
ex5.10.dat
Input data format FREE
UNIVARIATE PROPORTIONS AND COUNTS FOR CATEGORICAL VARIABLES
U1A
Category 1 0.316 158.000
Category 2 0.684 342.000
U1B
Category 1 0.282 141.000
Category 2 0.718 359.000
U1C
Category 1 0.312 156.000
Category 2 0.688 344.000
U2A
Category 1 0.718 359.000
Category 2 0.282 141.000
U2B
Category 1 0.704 352.000
Category 2 0.296 148.000
U2C
Category 1 0.694 347.000
Category 2 0.306 153.000
THE MODEL ESTIMATION TERMINATED NORMALLY
MODEL FIT INFORMATION
Number of Free Parameters 5
Chi-Square Test of Model Fit
Value 15.119*
Degrees of Freedom 16
P-Value 0.5159
* 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 0.039
Probability RMSEA <= .05 0.992
CFI/TLI
CFI 1.000
TLI 1.000
Chi-Square Test of Model Fit for the Baseline Model
Value 406.672
Degrees of Freedom 15
P-Value 0.0000
SRMR (Standardized Root Mean Square Residual)
Value 0.044
Optimum Function Value for Weighted Least-Squares Estimator
Value 0.11568994D-01
MODEL RESULTS
Two-Tailed
Estimate S.E. Est./S.E. P-Value
F1 BY
U1A 1.000 0.000 999.000 999.000
U1B 1.000 0.000 999.000 999.000
U1C 1.000 0.000 999.000 999.000
F2 BY
U2A 1.000 0.000 999.000 999.000
U2B 1.000 0.000 999.000 999.000
U2C 1.000 0.000 999.000 999.000
F2 WITH
F1 0.176 0.040 4.400 0.000
Thresholds
U1A$1 -0.515 0.043 -11.874 0.000
U1B$1 -0.515 0.043 -11.874 0.000
U1C$1 -0.515 0.043 -11.874 0.000
U2A$1 0.540 0.044 12.247 0.000
U2B$1 0.540 0.044 12.247 0.000
U2C$1 0.540 0.044 12.247 0.000
Variances
F1 0.494 0.043 11.453 0.000
F2 0.526 0.044 12.078 0.000
QUALITY OF NUMERICAL RESULTS
Condition Number for the Information Matrix 0.504E+00
(ratio of smallest to largest eigenvalue)
R-SQUARE
Observed Residual
Variable Estimate Variance
U1A 0.494 0.506
U1B 0.494 0.506
U1C 0.494 0.506
U2A 0.526 0.474
U2B 0.526 0.474
U2C 0.526 0.474
Beginning Time: 23:11:08
Ending Time: 23:11:08
Elapsed Time: 00:00:00
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