Mplus VERSION 8.8
MUTHEN & MUTHEN
04/19/2022 11:20 PM
INPUT INSTRUCTIONS
TITLE: this is an example of a linear
growth model for a continuous outcome
with time-invariant and time-varying
covariates carried out as a two-level
growth model using the DATA WIDETOLONG command
DATA: FILE IS ex9.16.dat;
DATA WIDETOLONG:
WIDE = y11-y14 | a31-a34;
LONG = y | a3;
IDVARIABLE = person;
REPETITION = time;
VARIABLE: NAMES ARE y11-y14 x1 x2 a31-a34;
USEVARIABLE = x1 x2 y a3 person time;
CLUSTER = person;
WITHIN = time a3;
BETWEEN = x1 x2;
ANALYSIS: TYPE = TWOLEVEL RANDOM;
MODEL: %WITHIN%
s | y ON time;
y ON a3;
%BETWEEN%
y s on x1 x2;
y WITH s;
INPUT READING TERMINATED NORMALLY
this is an example of a linear
growth model for a continuous outcome
with time-invariant and time-varying
covariates carried out as a two-level
growth model using the DATA WIDETOLONG command
SUMMARY OF ANALYSIS
Number of groups 1
Number of observations 2000
Number of dependent variables 1
Number of independent variables 4
Number of continuous latent variables 1
Observed dependent variables
Continuous
Y
Observed independent variables
X1 X2 A3 TIME
Continuous latent variables
S
Variables with special functions
Cluster variable PERSON
Within variables
A3 TIME
Between variables
X1 X2
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)
ex9.16.dat
Input data format FREE
SUMMARY OF DATA
Number of clusters 500
Average cluster size 4.000
Estimated Intraclass Correlations for the Y Variables
Intraclass Intraclass
Variable Correlation Variable Correlation
Y 0.615
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 2.195 0.543 -7.203 0.05% -0.064 1.300 1.901
2000.000 7.331 0.489 12.686 0.05% 2.579 4.320
A3 -0.007 -0.072 -3.990 0.05% -0.811 -0.275 -0.036
2000.000 0.958 -0.048 3.446 0.05% 0.265 0.841
TIME 1.500 0.000 0.000 25.00% 0.000 1.000 1.500
2000.000 1.250 -1.360 3.000 25.00% 2.000 3.000
X1 -0.073 0.041 -2.518 0.20% -0.928 -0.356 -0.050
500.000 0.990 -0.354 2.797 0.20% 0.175 0.743
X2 0.127 0.256 -2.012 0.20% -0.657 -0.155 0.061
500.000 0.945 -0.004 3.176 0.20% 0.300 0.938
THE MODEL ESTIMATION TERMINATED NORMALLY
MODEL FIT INFORMATION
Number of Free Parameters 11
Loglikelihood
H0 Value -3075.852
H0 Scaling Correction Factor 1.0035
for MLR
Information Criteria
Akaike (AIC) 6173.704
Bayesian (BIC) 6235.314
Sample-Size Adjusted BIC 6200.366
(n* = (n + 2) / 24)
MODEL RESULTS
Two-Tailed
Estimate S.E. Est./S.E. P-Value
Within Level
Y ON
A3 0.297 0.022 13.294 0.000
Residual Variances
Y 0.542 0.024 22.155 0.000
Between Level
S ON
X1 0.263 0.027 9.801 0.000
X2 0.473 0.025 18.909 0.000
Y ON
X1 0.561 0.054 10.296 0.000
X2 0.717 0.054 13.264 0.000
Y WITH
S 0.051 0.033 1.567 0.117
Intercepts
Y 0.570 0.055 10.400 0.000
S 1.010 0.025 39.763 0.000
Residual Variances
Y 1.079 0.093 11.622 0.000
S 0.203 0.020 10.237 0.000
QUALITY OF NUMERICAL RESULTS
Condition Number for the Information Matrix 0.379E-01
(ratio of smallest to largest eigenvalue)
Beginning Time: 23:20:18
Ending Time: 23:20:18
Elapsed Time: 00:00:00
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