Mplus VERSION 8.8
MUTHEN & MUTHEN
04/19/2022 11:20 PM
INPUT INSTRUCTIONS
TITLE: this is an example of a two-level
regression analysis for a continuous
dependent variable with a random intercept and a latent covariate
DATA: FILE = ex9.1b.dat;
VARIABLE: NAMES = y x w clus;
BETWEEN = w;
CLUSTER = clus;
DEFINE: CENTER x (GRANDMEAN);
ANALYSIS: TYPE = TWOLEVEL;
MODEL:
%WITHIN%
y ON x (gamma10);
%BETWEEN%
y ON w
x (gamma01);
MODEL CONSTRAINT:
NEW(betac);
betac = gamma01 - gamma10;
INPUT READING TERMINATED NORMALLY
this is an example of a two-level
regression analysis for a continuous
dependent variable with a random intercept and a latent covariate
SUMMARY OF ANALYSIS
Number of groups 1
Number of observations 1000
Number of dependent variables 1
Number of independent variables 2
Number of continuous latent variables 0
Observed dependent variables
Continuous
Y
Observed independent variables
X W
Variables with special functions
Cluster variable CLUS
Between variables
W
Centering (GRANDMEAN)
X
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)
ex9.1b.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
Variable Correlation
Y 0.509
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
Y 1.958 0.122 -3.316 0.10% 0.341 1.420 1.905
1000.000 3.465 -0.137 7.975 0.10% 2.343 3.535
X 0.000 0.084 -3.834 0.10% -0.977 -0.324 -0.049
1000.000 1.435 0.210 4.968 0.10% 0.276 1.028
W -0.082 0.120 -2.498 0.91% -0.831 -0.384 -0.177
110.000 1.004 -0.060 2.343 0.91% 0.079 0.638
THE MODEL ESTIMATION TERMINATED NORMALLY
MODEL FIT INFORMATION
Number of Free Parameters 6
Loglikelihood
H0 Value -2971.585
H0 Scaling Correction Factor 0.9064
for MLR
H1 Value -2971.585
H1 Scaling Correction Factor 0.9064
for MLR
Information Criteria
Akaike (AIC) 5955.170
Bayesian (BIC) 5984.617
Sample-Size Adjusted BIC 5965.560
(n* = (n + 2) / 24)
Chi-Square Test of Model Fit
Value 0.001*
Degrees of Freedom 0
P-Value 0.0000
Scaling Correction Factor 1.0000
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 601.098
Degrees of Freedom 3
P-Value 0.0000
SRMR (Standardized Root Mean Square Residual)
Value for Within 0.000
Value for Between 0.001
MODEL RESULTS
Two-Tailed
Estimate S.E. Est./S.E. P-Value
Within Level
Y ON
X 0.780 0.032 24.136 0.000
Residual Variances
Y 0.975 0.043 22.684 0.000
Between Level
Y ON
W 0.385 0.122 3.157 0.002
X 1.188 0.177 6.730 0.000
Intercepts
Y 2.005 0.078 25.545 0.000
Residual Variances
Y 0.519 0.085 6.114 0.000
New/Additional Parameters
BETAC 0.408 0.181 2.256 0.024
QUALITY OF NUMERICAL RESULTS
Condition Number for the Information Matrix 0.110E-02
(ratio of smallest to largest eigenvalue)
Beginning Time: 23:20:44
Ending Time: 23:20:44
Elapsed Time: 00:00:00
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