Mplus VERSION 8.8
MUTHEN & MUTHEN
04/19/2022 11:56 PM
INPUT INSTRUCTIONS
TITLE: this is an example of two-level CFA with
continuous factor indicators, covariates,
and random slopes
DATA: FILE IS ex9.8.dat;
VARIABLE: NAMES ARE y1-y4 x1 x2 w clus;
CLUSTER = clus;
WITHIN = x1 x2;
BETWEEN = w;
ANALYSIS: TYPE = TWOLEVEL RANDOM;
MODEL:
%WITHIN%
fw BY y1-y4;
s1 | fw ON x1;
s2 | fw ON x2;
%BETWEEN%
fb BY y1-y4;
y1-y4@0;
fb s1 s2 ON w;
INPUT READING TERMINATED NORMALLY
this is an example of two-level CFA with
continuous factor indicators, covariates,
and random slopes
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 4
Observed dependent variables
Continuous
Y1 Y2 Y3 Y4
Observed independent variables
X1 X2 W
Continuous latent variables
FW S1 S2 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)
ex9.8.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 0.311 Y2 0.308 Y3 0.309
Y4 0.327
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 -0.049 -0.002 -8.015 0.10% -2.063 -0.637 -0.042
1000.000 6.006 0.096 7.694 0.10% 0.574 2.000
Y2 -0.066 0.093 -7.121 0.10% -1.973 -0.696 -0.031
1000.000 5.346 0.111 7.076 0.10% 0.492 1.787
Y3 -0.125 0.119 -7.005 0.10% -2.164 -0.676 -0.118
1000.000 5.722 0.111 7.888 0.10% 0.452 1.822
Y4 -0.049 0.136 -7.629 0.10% -1.956 -0.690 -0.155
1000.000 5.605 0.147 8.394 0.10% 0.437 1.965
X1 -0.038 0.007 -3.245 0.10% -0.875 -0.312 -0.039
1000.000 1.009 -0.006 3.794 0.10% 0.218 0.780
X2 0.033 -0.056 -3.263 0.10% -0.769 -0.228 -0.001
1000.000 0.959 -0.109 2.962 0.10% 0.303 0.863
W -0.070 -0.100 -2.347 0.91% -1.052 -0.306 -0.081
110.000 1.138 -0.513 2.355 0.91% 0.249 0.840
THE MODEL ESTIMATION TERMINATED NORMALLY
MODEL FIT INFORMATION
Number of Free Parameters 23
Loglikelihood
H0 Value -6752.349
H0 Scaling Correction Factor 0.9897
for MLR
Information Criteria
Akaike (AIC) 13550.698
Bayesian (BIC) 13663.577
Sample-Size Adjusted BIC 13590.528
(n* = (n + 2) / 24)
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 0.966 0.028 34.660 0.000
Y3 0.996 0.031 32.169 0.000
Y4 0.968 0.026 37.750 0.000
Residual Variances
Y1 1.134 0.064 17.605 0.000
Y2 0.909 0.049 18.374 0.000
Y3 0.980 0.049 19.941 0.000
Y4 0.969 0.056 17.410 0.000
FW 1.200 0.081 14.743 0.000
Between Level
FB BY
Y1 1.000 0.000 999.000 999.000
Y2 0.934 0.036 25.863 0.000
Y3 0.970 0.033 29.172 0.000
Y4 0.987 0.042 23.278 0.000
FB ON
W 1.042 0.080 12.971 0.000
S1 ON
W 0.510 0.086 5.964 0.000
S2 ON
W 0.101 0.078 1.285 0.199
Intercepts
Y1 0.058 0.090 0.641 0.522
Y2 0.033 0.084 0.399 0.690
Y3 -0.022 0.091 -0.238 0.812
Y4 0.056 0.087 0.652 0.514
S1 0.560 0.087 6.421 0.000
S2 0.471 0.091 5.174 0.000
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
S1 0.524 0.097 5.409 0.000
S2 0.583 0.129 4.516 0.000
FB 0.568 0.127 4.460 0.000
QUALITY OF NUMERICAL RESULTS
Condition Number for the Information Matrix 0.694E-02
(ratio of smallest to largest eigenvalue)
Beginning Time: 23:56:39
Ending Time: 23:56:40
Elapsed Time: 00:00:01
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