Mplus VERSION 8.8
MUTHEN & MUTHEN
04/19/2022 11:12 PM
INPUT INSTRUCTIONS
TITLE: this is an example of a linear growth
model for a categorical outcome using the
Theta parameterization
DATA: FILE IS ex6.5.dat;
VARIABLE: NAMES ARE u11-u14;
CATEGORICAL ARE u11-u14;
ANALYSIS: PARAMETERIZATION = THETA;
MODEL: i s | u11@0 u12@1 u13@2 u14@3;
INPUT READING TERMINATED NORMALLY
this is an example of a linear growth
model for a categorical outcome using the
Theta parameterization
SUMMARY OF ANALYSIS
Number of groups 1
Number of observations 500
Number of dependent variables 4
Number of independent variables 0
Number of continuous latent variables 2
Observed dependent variables
Binary and ordered categorical (ordinal)
U11 U12 U13 U14
Continuous latent variables
I S
Estimator WLSMV
Maximum number of iterations 1000
Convergence criterion 0.500D-04
Maximum number of steepest descent iterations 20
Parameterization THETA
Link PROBIT
Input data file(s)
ex6.5.dat
Input data format FREE
UNIVARIATE PROPORTIONS AND COUNTS FOR CATEGORICAL VARIABLES
U11
Category 1 0.236 118.000
Category 2 0.248 124.000
Category 3 0.264 132.000
Category 4 0.252 126.000
U12
Category 1 0.388 194.000
Category 2 0.282 141.000
Category 3 0.168 84.000
Category 4 0.162 81.000
U13
Category 1 0.594 297.000
Category 2 0.130 65.000
Category 3 0.148 74.000
Category 4 0.128 64.000
U14
Category 1 0.676 338.000
Category 2 0.126 63.000
Category 3 0.090 45.000
Category 4 0.108 54.000
THE MODEL ESTIMATION TERMINATED NORMALLY
MODEL FIT INFORMATION
Number of Free Parameters 10
Chi-Square Test of Model Fit
Value 12.014*
Degrees of Freedom 8
P-Value 0.1506
* 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 0.032
90 Percent C.I. 0.000 0.066
Probability RMSEA <= .05 0.777
CFI/TLI
CFI 0.992
TLI 0.994
Chi-Square Test of Model Fit for the Baseline Model
Value 491.173
Degrees of Freedom 6
P-Value 0.0000
SRMR (Standardized Root Mean Square Residual)
Value 0.021
Optimum Function Value for Weighted Least-Squares Estimator
Value 0.67589863D-02
MODEL RESULTS
Two-Tailed
Estimate S.E. Est./S.E. P-Value
I |
U11 1.000 0.000 999.000 999.000
U12 1.000 0.000 999.000 999.000
U13 1.000 0.000 999.000 999.000
U14 1.000 0.000 999.000 999.000
S |
U11 0.000 0.000 999.000 999.000
U12 1.000 0.000 999.000 999.000
U13 2.000 0.000 999.000 999.000
U14 3.000 0.000 999.000 999.000
S WITH
I -0.047 0.118 -0.400 0.689
Means
I 0.000 0.000 999.000 999.000
S -0.772 0.107 -7.189 0.000
Thresholds
U11$1 -1.144 0.152 -7.524 0.000
U11$2 -0.071 0.078 -0.913 0.361
U11$3 1.001 0.143 7.001 0.000
U12$1 -1.144 0.152 -7.524 0.000
U12$2 -0.071 0.078 -0.913 0.361
U12$3 1.001 0.143 7.001 0.000
U13$1 -1.144 0.152 -7.524 0.000
U13$2 -0.071 0.078 -0.913 0.361
U13$3 1.001 0.143 7.001 0.000
U14$1 -1.144 0.152 -7.524 0.000
U14$2 -0.071 0.078 -0.913 0.361
U14$3 1.001 0.143 7.001 0.000
Variances
I 1.451 0.500 2.900 0.004
S 0.313 0.135 2.312 0.021
Residual Variances
U11 1.000 0.000 999.000 999.000
U12 1.334 0.409 3.264 0.001
U13 2.417 0.688 3.513 0.000
U14 3.330 1.005 3.311 0.001
QUALITY OF NUMERICAL RESULTS
Condition Number for the Information Matrix 0.314E-03
(ratio of smallest to largest eigenvalue)
Beginning Time: 23:12:50
Ending Time: 23:12:50
Elapsed Time: 00:00:00
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