Mplus VERSION 8.8
MUTHEN & MUTHEN
04/19/2022 11:56 PM
INPUT INSTRUCTIONS
TITLE: this is an example of a two-level CFA with
continuous factor indicators, a random intercept
factor, and covariates
DATA: FILE IS ex9.6.dat;
VARIABLE: NAMES ARE y1-y4 x1 x2 w clus;
WITHIN = x1 x2;
BETWEEN = w;
CLUSTER = clus;
ANALYSIS: TYPE = TWOLEVEL;
MODEL:
%WITHIN%
fw BY y1-y4;
fw ON x1 x2;
%BETWEEN%
fb BY y1-y4;
y1-y4@0;
fb ON w;
INPUT READING TERMINATED NORMALLY
this is an example of a two-level CFA with
continuous factor indicators, a random intercept
factor, and covariates
SUMMARY OF ANALYSIS
Number of groups 1
Number of observations 1000
Number of dependent variables 4
Number of independent variables 3
Number of continuous latent variables 2
Observed dependent variables
Continuous
Y1 Y2 Y3 Y4
Observed independent variables
X1 X2 W
Continuous latent variables
FW FB
Variables with special functions
Cluster variable CLUS
Within variables
X1 X2
Between variables
W
Estimator MLR
Information matrix OBSERVED
Maximum number of iterations 100
Convergence criterion 0.100D-05
Maximum number of EM iterations 500
Convergence criteria for the EM algorithm
Loglikelihood change 0.100D-02
Relative loglikelihood change 0.100D-05
Derivative 0.100D-03
Minimum variance 0.100D-03
Maximum number of steepest descent iterations 20
Maximum number of iterations for H1 2000
Convergence criterion for H1 0.100D-03
Optimization algorithm EMA
Input data file(s)
ex9.6.dat
Input data format FREE
SUMMARY OF DATA
Number of clusters 110
Average cluster size 9.091
Estimated Intraclass Correlations for the Y Variables
Intraclass Intraclass Intraclass
Variable Correlation Variable Correlation Variable Correlation
Y1 0.125 Y2 0.121 Y3 0.106
Y4 0.115
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
Y1 -0.064 -0.030 -5.991 0.10% -1.687 -0.489 -0.031
1000.000 3.666 0.145 6.659 0.10% 0.391 1.525
Y2 -0.058 0.082 -6.610 0.10% -1.621 -0.604 -0.094
1000.000 3.591 0.231 7.316 0.10% 0.391 1.551
Y3 -0.026 0.084 -6.435 0.10% -1.613 -0.563 -0.115
1000.000 3.664 0.169 7.508 0.10% 0.394 1.637
Y4 -0.011 0.085 -6.885 0.10% -1.605 -0.627 -0.042
1000.000 3.730 -0.014 6.097 0.10% 0.436 1.614
X1 0.007 0.007 -2.649 0.10% -0.801 -0.241 -0.018
1000.000 0.985 -0.100 2.864 0.10% 0.245 0.829
X2 0.014 0.163 -2.962 0.10% -0.835 -0.308 -0.051
1000.000 1.017 -0.104 3.589 0.10% 0.237 0.864
W 0.006 0.035 -2.141 0.91% -0.726 -0.293 -0.051
110.000 0.815 -0.114 2.583 0.91% 0.309 0.737
THE MODEL ESTIMATION TERMINATED NORMALLY
MODEL FIT INFORMATION
Number of Free Parameters 19
Loglikelihood
H0 Value -6544.186
H0 Scaling Correction Factor 1.0294
for MLR
H1 Value -6542.255
H1 Scaling Correction Factor 0.9022
for MLR
Information Criteria
Akaike (AIC) 13126.372
Bayesian (BIC) 13219.620
Sample-Size Adjusted BIC 13159.275
(n* = (n + 2) / 24)
Chi-Square Test of Model Fit
Value 5.081*
Degrees of Freedom 17
P-Value 0.9975
Scaling Correction Factor 0.7601
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.
RMSEA (Root Mean Square Error Of Approximation)
Estimate 0.000
CFI/TLI
CFI 1.000
TLI 1.000
Chi-Square Test of Model Fit for the Baseline Model
Value 4111.076
Degrees of Freedom 24
P-Value 0.0000
SRMR (Standardized Root Mean Square Residual)
Value for Within 0.005
Value for Between 0.018
MODEL RESULTS
Two-Tailed
Estimate S.E. Est./S.E. P-Value
Within Level
FW BY
Y1 1.000 0.000 999.000 999.000
Y2 0.999 0.033 29.954 0.000
Y3 0.995 0.041 24.549 0.000
Y4 1.017 0.033 30.529 0.000
FW ON
X1 0.973 0.048 20.256 0.000
X2 0.510 0.039 13.017 0.000
Residual Variances
Y1 0.981 0.059 16.608 0.000
Y2 0.947 0.054 17.464 0.000
Y3 1.070 0.057 18.714 0.000
Y4 1.014 0.059 17.193 0.000
FW 0.980 0.083 11.778 0.000
Between Level
FB BY
Y1 1.000 0.000 999.000 999.000
Y2 0.960 0.070 13.617 0.000
Y3 0.924 0.075 12.292 0.000
Y4 0.949 0.068 13.908 0.000
FB ON
W 0.344 0.079 4.339 0.000
Intercepts
Y1 -0.083 0.072 -1.143 0.253
Y2 -0.077 0.073 -1.058 0.290
Y3 -0.045 0.069 -0.647 0.518
Y4 -0.030 0.072 -0.413 0.679
Residual Variances
Y1 0.000 0.000 999.000 999.000
Y2 0.000 0.000 999.000 999.000
Y3 0.000 0.000 999.000 999.000
Y4 0.000 0.000 999.000 999.000
FB 0.361 0.072 5.041 0.000
QUALITY OF NUMERICAL RESULTS
Condition Number for the Information Matrix 0.178E-01
(ratio of smallest to largest eigenvalue)
Beginning Time: 23:56:35
Ending Time: 23:56:35
Elapsed Time: 00:00:00
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