Mplus VERSION 8.8
MUTHEN & MUTHEN
04/19/2022 11:18 PM
INPUT INSTRUCTIONS
TITLE: this is an example of a two-level
multiple group CFA with continuous
factor indicators
DATA: FILE IS ex9.11.dat;
VARIABLE: NAMES ARE y1-y6 g clus;
GROUPING = g (1 = g1 2 = g2);
CLUSTER = clus;
ANALYSIS: TYPE = TWOLEVEL;
MODEL:
%WITHIN%
fw1 BY y1-y3;
fw2 BY y4-y6;
%BETWEEN%
fb1 BY y1-y3;
fb2 BY y4-y6;
MODEL g2:
%WITHIN%
fw1 BY y2-y3;
fw2 BY y5-y6;
INPUT READING TERMINATED NORMALLY
this is an example of a two-level
multiple group CFA with continuous
factor indicators
SUMMARY OF ANALYSIS
Number of groups 2
Number of observations
Group G1 1000
Group G2 800
Total sample size 1800
Number of dependent variables 6
Number of independent variables 0
Number of continuous latent variables 4
Observed dependent variables
Continuous
Y1 Y2 Y3 Y4 Y5 Y6
Continuous latent variables
FW1 FW2 FB1 FB2
Variables with special functions
Grouping variable G
Cluster variable CLUS
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)
ex9.11.dat
Input data format FREE
SUMMARY OF DATA
Group G1
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 0.234 Y2 0.223 Y3 0.177
Y4 0.192 Y5 0.171 Y6 0.220
Group G2
Number of clusters 120
Average cluster size 6.667
Estimated Intraclass Correlations for the Y Variables
Intraclass Intraclass Intraclass
Variable Correlation Variable Correlation Variable Correlation
Y1 0.227 Y2 0.276 Y3 0.236
Y4 0.221 Y5 0.234 Y6 0.215
UNIVARIATE SAMPLE STATISTICS
UNIVARIATE HIGHER-ORDER MOMENT DESCRIPTIVE STATISTICS FOR G1
Variable/ Mean/ Skewness/ Minimum/ % with Percentiles
Sample Size Variance Kurtosis Maximum Min/Max 20%/60% 40%/80% Median
Y1 0.039 -0.038 -10.482 0.10% -2.287 -0.727 0.023
1000.000 7.547 0.027 9.270 0.10% 0.760 2.384
Y2 -0.048 0.036 -8.051 0.10% -2.546 -0.698 -0.098
1000.000 7.853 -0.119 9.519 0.10% 0.586 2.386
Y3 0.018 -0.012 -8.469 0.10% -2.363 -0.657 0.039
1000.000 7.613 -0.158 8.359 0.10% 0.794 2.309
Y4 -0.091 -0.053 -8.408 0.10% -2.320 -0.776 -0.005
1000.000 7.125 -0.139 8.326 0.10% 0.645 2.159
Y5 -0.251 -0.052 -8.861 0.10% -2.681 -0.849 -0.189
1000.000 7.721 -0.159 7.993 0.10% 0.475 2.019
Y6 0.079 -0.027 -9.090 0.10% -2.255 -0.716 0.010
1000.000 7.587 -0.134 9.057 0.10% 0.762 2.457
UNIVARIATE HIGHER-ORDER MOMENT DESCRIPTIVE STATISTICS FOR G2
Variable/ Mean/ Skewness/ Minimum/ % with Percentiles
Sample Size Variance Kurtosis Maximum Min/Max 20%/60% 40%/80% Median
Y1 0.023 -0.024 -8.121 0.12% -2.387 -0.549 0.050
800.000 7.811 -0.067 9.895 0.12% 0.738 2.360
Y2 -0.043 0.035 -7.130 0.12% -2.249 -0.749 -0.050
800.000 6.071 -0.246 7.398 0.12% 0.678 2.023
Y3 0.034 -0.014 -9.265 0.12% -2.107 -0.612 -0.039
800.000 6.257 -0.224 6.839 0.12% 0.697 2.216
Y4 -0.083 -0.024 -9.785 0.12% -2.370 -0.873 -0.131
800.000 7.503 0.152 8.708 0.12% 0.646 2.272
Y5 -0.115 0.050 -8.827 0.12% -2.345 -0.846 -0.299
800.000 7.110 -0.053 7.914 0.12% 0.606 2.128
Y6 0.123 0.064 -7.570 0.12% -2.155 -0.526 0.050
800.000 6.789 -0.091 9.445 0.12% 0.737 2.425
THE MODEL ESTIMATION TERMINATED NORMALLY
MODEL FIT INFORMATION
Number of Free Parameters 56
Loglikelihood
H0 Value -24926.956
H0 Scaling Correction Factor 1.0118
for MLR
H1 Value -24909.987
H1 Scaling Correction Factor 0.9597
for MLR
Information Criteria
Akaike (AIC) 49965.913
Bayesian (BIC) 50273.663
Sample-Size Adjusted BIC 50095.754
(n* = (n + 2) / 24)
Chi-Square Test of Model Fit
Value 38.271*
Degrees of Freedom 40
P-Value 0.5483
Scaling Correction Factor 0.8868
for MLR
Chi-Square Contribution From Each Group
G1 15.435
G2 22.835
* 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 1130.168
Degrees of Freedom 60
P-Value 0.0000
SRMR (Standardized Root Mean Square Residual)
Value for Within 0.010
Value for Between 0.074
MODEL RESULTS
Two-Tailed
Estimate S.E. Est./S.E. P-Value
Group G1
Within Level
FW1 BY
Y1 1.000 0.000 999.000 999.000
Y2 1.129 0.117 9.681 0.000
Y3 1.218 0.132 9.233 0.000
FW2 BY
Y4 1.000 0.000 999.000 999.000
Y5 0.969 0.107 9.090 0.000
Y6 1.083 0.146 7.412 0.000
FW2 WITH
FW1 0.948 0.151 6.264 0.000
Variances
FW1 1.590 0.254 6.261 0.000
FW2 1.795 0.310 5.797 0.000
Residual Variances
Y1 4.139 0.246 16.841 0.000
Y2 4.089 0.252 16.205 0.000
Y3 3.898 0.291 13.399 0.000
Y4 3.953 0.277 14.250 0.000
Y5 4.715 0.240 19.650 0.000
Y6 3.797 0.271 13.991 0.000
Between Level
FB1 BY
Y1 1.000 0.000 999.000 999.000
Y2 1.382 0.893 1.547 0.122
Y3 0.587 0.172 3.404 0.001
FB2 BY
Y4 1.000 0.000 999.000 999.000
Y5 0.799 0.702 1.138 0.255
Y6 0.942 1.056 0.893 0.372
FB2 WITH
FB1 0.053 0.219 0.244 0.807
Means
FB1 0.000 0.000 999.000 999.000
FB2 0.000 0.000 999.000 999.000
Intercepts
Y1 0.046 0.131 0.349 0.727
Y2 -0.044 0.148 -0.299 0.765
Y3 0.078 0.108 0.721 0.471
Y4 -0.130 0.125 -1.037 0.300
Y5 -0.223 0.127 -1.756 0.079
Y6 0.076 0.143 0.535 0.593
Variances
FB1 0.707 0.530 1.335 0.182
FB2 0.462 0.594 0.777 0.437
Residual Variances
Y1 0.984 0.496 1.983 0.047
Y2 0.439 0.820 0.535 0.593
Y3 1.071 0.231 4.626 0.000
Y4 0.883 0.505 1.749 0.080
Y5 0.964 0.269 3.587 0.000
Y6 1.329 0.473 2.808 0.005
Group G2
Within Level
FW1 BY
Y1 1.000 0.000 999.000 999.000
Y2 0.576 0.107 5.395 0.000
Y3 0.519 0.100 5.198 0.000
FW2 BY
Y4 1.000 0.000 999.000 999.000
Y5 0.690 0.100 6.901 0.000
Y6 0.754 0.093 8.121 0.000
FW2 WITH
FW1 1.054 0.214 4.934 0.000
Variances
FW1 2.530 0.512 4.938 0.000
FW2 2.663 0.421 6.321 0.000
Residual Variances
Y1 3.484 0.532 6.554 0.000
Y2 3.483 0.217 16.068 0.000
Y3 4.072 0.238 17.139 0.000
Y4 3.113 0.368 8.458 0.000
Y5 4.200 0.312 13.454 0.000
Y6 3.808 0.250 15.256 0.000
Between Level
FB1 BY
Y1 1.000 0.000 999.000 999.000
Y2 1.382 0.893 1.547 0.122
Y3 0.587 0.172 3.404 0.001
FB2 BY
Y4 1.000 0.000 999.000 999.000
Y5 0.799 0.702 1.138 0.255
Y6 0.942 1.056 0.893 0.372
FB2 WITH
FB1 0.042 0.279 0.151 0.880
Means
FB1 -0.015 0.148 -0.098 0.922
FB2 0.096 0.163 0.586 0.558
Intercepts
Y1 0.046 0.131 0.349 0.727
Y2 -0.044 0.148 -0.299 0.765
Y3 0.078 0.108 0.721 0.471
Y4 -0.130 0.125 -1.037 0.300
Y5 -0.223 0.127 -1.756 0.079
Y6 0.076 0.143 0.535 0.593
Variances
FB1 0.656 0.387 1.696 0.090
FB2 0.408 0.544 0.750 0.453
Residual Variances
Y1 1.162 0.422 2.756 0.006
Y2 0.399 0.922 0.432 0.665
Y3 1.244 0.280 4.449 0.000
Y4 1.210 0.496 2.441 0.015
Y5 1.452 0.323 4.502 0.000
Y6 1.052 0.409 2.571 0.010
QUALITY OF NUMERICAL RESULTS
Condition Number for the Information Matrix 0.236E-02
(ratio of smallest to largest eigenvalue)
Beginning Time: 23:18:45
Ending Time: 23:18:46
Elapsed Time: 00:00:01
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