Mplus VERSION 6
MUTHEN & MUTHEN
04/25/2010 10:58 PM
INPUT INSTRUCTIONS
TITLE:
cont7
A MIMIC model.
Complex sample analysis (aggregated or marginal model),
taking clustering (non-independence of observations) into
account. Compare results to wMimic1 (same MIMIC model).
For related work, see, e.g.:
Muthen & Satorra (1995b) as referred to in the Mplus manual
DATA:
FILE IS school.dat;
VARIABLE:
NAMES ARE x1 y1-y16 x2 school x3 x4;
USEV ARE y6-y9 x1 x2 x3 x4 school;
CLUSTER IS school;
ANALYSIS:
TYPE = MEANSTRUCTURE COMPLEX;
MODEL:
f BY y6-y9;
f ON x1-x4;
OUTPUT: STANDARDIZED;
*** WARNING in ANALYSIS command
Starting with Version 5, TYPE=MEANSTRUCTURE is the default for all
analyses. To remove means from the model, use
MODEL=NOMEANSTRUCTURE in the ANALYSIS command.
1 WARNING(S) FOUND IN THE INPUT INSTRUCTIONS
cont7
A MIMIC model.
Complex sample analysis (aggregated or marginal model),
taking clustering (non-independence of observations) into
account. Compare results to wMimic1 (same MIMIC model).
For related work, see, e.g.:
Muthen & Satorra (1995b) as referred to in the Mplus manual
SUMMARY OF ANALYSIS
Number of groups 1
Number of observations 5198
Number of dependent variables 4
Number of independent variables 4
Number of continuous latent variables 1
Observed dependent variables
Continuous
Y6 Y7 Y8 Y9
Observed independent variables
X1 X2 X3 X4
Continuous latent variables
F
Variables with special functions
Cluster variable SCHOOL
Estimator MLR
Information matrix OBSERVED
Maximum number of iterations 1000
Convergence criterion 0.500D-04
Maximum number of steepest descent iterations 20
Input data file(s)
school.dat
Input data format FREE
SUMMARY OF DATA
Number of clusters 235
THE MODEL ESTIMATION TERMINATED NORMALLY
TESTS OF MODEL FIT
Chi-Square Test of Model Fit
Value 54.091*
Degrees of Freedom 14
P-Value 0.0000
Scaling Correction Factor 1.432
for MLR
* 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.
Chi-Square Test of Model Fit for the Baseline Model
Value 8273.087
Degrees of Freedom 22
P-Value 0.0000
CFI/TLI
CFI 0.995
TLI 0.992
Loglikelihood
H0 Value -56157.382
H0 Scaling Correction Factor 1.561
for MLR
H1 Value -56118.652
H1 Scaling Correction Factor 1.501
for MLR
Information Criteria
Number of Free Parameters 16
Akaike (AIC) 112346.764
Bayesian (BIC) 112451.661
Sample-Size Adjusted BIC 112400.818
(n* = (n + 2) / 24)
RMSEA (Root Mean Square Error Of Approximation)
Estimate 0.023
90 Percent C.I. 0.017 0.030
Probability RMSEA <= .05 1.000
SRMR (Standardized Root Mean Square Residual)
Value 0.013
MODEL RESULTS
Two-Tailed
Estimate S.E. Est./S.E. P-Value
F BY
Y6 1.000 0.000 999.000 999.000
Y7 1.053 0.017 63.496 0.000
Y8 0.659 0.020 32.656 0.000
Y9 1.050 0.028 38.081 0.000
F ON
X1 0.327 0.019 17.014 0.000
X2 0.045 0.030 1.475 0.140
X3 -0.115 0.012 -9.564 0.000
X4 -0.126 0.060 -2.087 0.037
Intercepts
Y6 2.349 0.121 19.473 0.000
Y7 2.469 0.128 19.277 0.000
Y8 2.073 0.083 25.088 0.000
Y9 2.791 0.132 21.084 0.000
Residual Variances
Y6 0.353 0.014 24.758 0.000
Y7 0.267 0.014 18.882 0.000
Y8 1.382 0.023 58.902 0.000
Y9 2.529 0.052 48.671 0.000
F 0.768 0.028 27.209 0.000
STANDARDIZED MODEL RESULTS
STDYX Standardization
Two-Tailed
Estimate S.E. Est./S.E. P-Value
F BY
Y6 0.860 0.007 117.404 0.000
Y7 0.898 0.006 154.517 0.000
Y8 0.490 0.014 34.699 0.000
Y9 0.552 0.012 44.322 0.000
F ON
X1 0.357 0.018 19.736 0.000
X2 0.022 0.015 1.472 0.141
X3 -0.244 0.027 -8.896 0.000
X4 -0.050 0.024 -2.077 0.038
Intercepts
Y6 2.016 0.109 18.536 0.000
Y7 2.101 0.115 18.331 0.000
Y8 1.537 0.064 24.167 0.000
Y9 1.464 0.071 20.549 0.000
Residual Variances
Y6 0.260 0.013 20.629 0.000
Y7 0.194 0.010 18.545 0.000
Y8 0.760 0.014 54.873 0.000
Y9 0.695 0.014 50.605 0.000
F 0.764 0.017 45.170 0.000
STDY Standardization
Two-Tailed
Estimate S.E. Est./S.E. P-Value
F BY
Y6 0.860 0.007 117.404 0.000
Y7 0.898 0.006 154.517 0.000
Y8 0.490 0.014 34.699 0.000
Y9 0.552 0.012 44.322 0.000
F ON
X1 0.326 0.016 19.912 0.000
X2 0.045 0.030 1.472 0.141
X3 -0.114 0.012 -9.441 0.000
X4 -0.125 0.060 -2.090 0.037
Intercepts
Y6 2.016 0.109 18.536 0.000
Y7 2.101 0.115 18.331 0.000
Y8 1.537 0.064 24.167 0.000
Y9 1.464 0.071 20.549 0.000
Residual Variances
Y6 0.260 0.013 20.629 0.000
Y7 0.194 0.010 18.545 0.000
Y8 0.760 0.014 54.873 0.000
Y9 0.695 0.014 50.605 0.000
F 0.764 0.017 45.170 0.000
STD Standardization
Two-Tailed
Estimate S.E. Est./S.E. P-Value
F BY
Y6 1.003 0.018 54.896 0.000
Y7 1.055 0.014 73.970 0.000
Y8 0.661 0.023 28.744 0.000
Y9 1.052 0.027 38.367 0.000
F ON
X1 0.326 0.016 19.912 0.000
X2 0.045 0.030 1.472 0.141
X3 -0.114 0.012 -9.441 0.000
X4 -0.125 0.060 -2.090 0.037
Intercepts
Y6 2.349 0.121 19.473 0.000
Y7 2.469 0.128 19.277 0.000
Y8 2.073 0.083 25.088 0.000
Y9 2.791 0.132 21.084 0.000
Residual Variances
Y6 0.353 0.014 24.758 0.000
Y7 0.267 0.014 18.882 0.000
Y8 1.382 0.023 58.902 0.000
Y9 2.529 0.052 48.671 0.000
F 0.764 0.017 45.170 0.000
R-SQUARE
Observed Two-Tailed
Variable Estimate S.E. Est./S.E. P-Value
Y6 0.740 0.013 58.702 0.000
Y7 0.806 0.010 77.259 0.000
Y8 0.240 0.014 17.350 0.000
Y9 0.305 0.014 22.161 0.000
Latent Two-Tailed
Variable Estimate S.E. Est./S.E. P-Value
F 0.236 0.017 13.931 0.000
QUALITY OF NUMERICAL RESULTS
Condition Number for the Information Matrix 0.370E-03
(ratio of smallest to largest eigenvalue)
Beginning Time: 22:58:08
Ending Time: 22:58:09
Elapsed Time: 00:00:01
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