Mplus VERSION 8.8
MUTHEN & MUTHEN
04/19/2022 11:12 PM
INPUT INSTRUCTIONS
TITLE: this is an example of a multiple indicator
linear growth model for continuous outcomes
DATA: FILE IS ex6.14.dat;
VARIABLE: NAMES ARE y11 y21 y31 y12 y22 y32 y13
y23 y33;
MODEL: f1 BY y11
y21-y31 (1-2);
f2 BY y12
y22-y32 (1-2);
f3 BY y13
y23-y33 (1-2);
[y11 y12 y13] (3);
[y21 y22 y23] (4);
[y31 y32 y33] (5);
i s | f1@0 f2@1 f3@2;
INPUT READING TERMINATED NORMALLY
this is an example of a multiple indicator
linear growth model for continuous outcomes
SUMMARY OF ANALYSIS
Number of groups 1
Number of observations 500
Number of dependent variables 9
Number of independent variables 0
Number of continuous latent variables 5
Observed dependent variables
Continuous
Y11 Y21 Y31 Y12 Y22 Y32
Y13 Y23 Y33
Continuous latent variables
F1 F2 F3 I S
Estimator ML
Information matrix OBSERVED
Maximum number of iterations 1000
Convergence criterion 0.500D-04
Maximum number of steepest descent iterations 20
Input data file(s)
ex6.14.dat
Input data format FREE
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
Y11 -0.549 -0.070 -4.931 0.20% -1.618 -0.909 -0.574
500.000 1.850 0.161 3.355 0.20% -0.287 0.622
Y21 -0.014 -0.177 -4.347 0.20% -1.238 -0.355 0.003
500.000 2.044 -0.060 3.758 0.20% 0.428 1.177
Y31 0.549 -0.046 -3.981 0.20% -0.665 0.155 0.571
500.000 1.876 -0.033 4.534 0.20% 0.987 1.661
Y12 0.512 -0.196 -3.937 0.20% -0.711 0.172 0.597
500.000 2.096 -0.108 4.049 0.20% 0.927 1.747
Y22 1.035 0.020 -3.365 0.20% -0.234 0.696 1.084
500.000 2.225 -0.022 5.289 0.20% 1.348 2.278
Y32 1.536 0.096 -3.476 0.20% 0.329 1.105 1.440
500.000 2.153 -0.092 5.398 0.20% 1.855 2.738
Y13 1.556 0.057 -3.977 0.20% 0.122 1.077 1.509
500.000 2.677 -0.033 5.927 0.20% 1.966 2.830
Y23 2.018 -0.191 -3.841 0.20% 0.740 1.682 2.035
500.000 2.339 0.434 6.616 0.20% 2.454 3.299
Y33 2.489 0.023 -1.905 0.20% 1.058 2.062 2.510
500.000 2.336 -0.293 6.718 0.20% 2.927 3.791
THE MODEL ESTIMATION TERMINATED NORMALLY
MODEL FIT INFORMATION
Number of Free Parameters 21
Loglikelihood
H0 Value -6357.999
H1 Value -6339.524
Information Criteria
Akaike (AIC) 12757.998
Bayesian (BIC) 12846.504
Sample-Size Adjusted BIC 12779.849
(n* = (n + 2) / 24)
Chi-Square Test of Model Fit
Value 36.949
Degrees of Freedom 33
P-Value 0.2914
RMSEA (Root Mean Square Error Of Approximation)
Estimate 0.015
90 Percent C.I. 0.000 0.037
Probability RMSEA <= .05 0.998
CFI/TLI
CFI 0.999
TLI 0.999
Chi-Square Test of Model Fit for the Baseline Model
Value 3565.847
Degrees of Freedom 36
P-Value 0.0000
SRMR (Standardized Root Mean Square Residual)
Value 0.023
MODEL RESULTS
Two-Tailed
Estimate S.E. Est./S.E. P-Value
I |
F1 1.000 0.000 999.000 999.000
F2 1.000 0.000 999.000 999.000
F3 1.000 0.000 999.000 999.000
S |
F1 0.000 0.000 999.000 999.000
F2 1.000 0.000 999.000 999.000
F3 2.000 0.000 999.000 999.000
F1 BY
Y11 1.000 0.000 999.000 999.000
Y21 1.003 0.018 56.519 0.000
Y31 0.960 0.018 53.259 0.000
F2 BY
Y12 1.000 0.000 999.000 999.000
Y22 1.003 0.018 56.519 0.000
Y32 0.960 0.018 53.259 0.000
F3 BY
Y13 1.000 0.000 999.000 999.000
Y23 1.003 0.018 56.519 0.000
Y33 0.960 0.018 53.259 0.000
S WITH
I -0.102 0.066 -1.547 0.122
Means
I 0.000 0.000 999.000 999.000
S 1.023 0.033 30.768 0.000
Intercepts
Y11 -0.519 0.059 -8.816 0.000
Y21 -0.017 0.059 -0.296 0.767
Y31 0.540 0.057 9.500 0.000
Y12 -0.519 0.059 -8.816 0.000
Y22 -0.017 0.059 -0.296 0.767
Y32 0.540 0.057 9.500 0.000
Y13 -0.519 0.059 -8.816 0.000
Y23 -0.017 0.059 -0.296 0.767
Y33 0.540 0.057 9.500 0.000
F1 0.000 0.000 999.000 999.000
F2 0.000 0.000 999.000 999.000
F3 0.000 0.000 999.000 999.000
Variances
I 1.125 0.129 8.714 0.000
S 0.269 0.060 4.470 0.000
Residual Variances
Y11 0.519 0.047 10.981 0.000
Y21 0.466 0.045 10.400 0.000
Y31 0.543 0.047 11.620 0.000
Y12 0.520 0.046 11.322 0.000
Y22 0.510 0.046 11.168 0.000
Y32 0.497 0.044 11.367 0.000
Y13 0.542 0.048 11.243 0.000
Y23 0.378 0.040 9.557 0.000
Y33 0.536 0.046 11.725 0.000
F1 0.329 0.111 2.962 0.003
F2 0.501 0.066 7.559 0.000
F3 0.210 0.126 1.664 0.096
QUALITY OF NUMERICAL RESULTS
Condition Number for the Information Matrix 0.237E-02
(ratio of smallest to largest eigenvalue)
Beginning Time: 23:12:17
Ending Time: 23:12:17
Elapsed Time: 00:00:00
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