Mplus VERSION 5.1
MUTHEN & MUTHEN
05/05/2008 8:26 PM
INPUT INSTRUCTIONS
TITLE:
cont5
Growth model for two parallel processes observed over
four time points. Regression of the slope in one process
on the intercept of the other and a covariate.
Non-linear growth for the first process by estimating the
last two timesteps. Linear growth for the second process.
First process: y11, y12, y13, y14
Second process: y21, y22, y23, y24
For related work, see:
Muthen, B. (1997). Latent variable modeling with longitudinal
and and multilevel data. In A. Raftery (ed.), Sociological
Methodology 1997 (pp. 453-480). Boston: Blackwell Publishers.
DATA:
FILE IS comp.dat;
FORMAT is 3f8 f8.4 8f8.2 3f8 2f8.2;
VARIABLE:
NAMES ARE g1 g2 cluster g3
y11-y14
y21-y24
x1-x5;
USEOBS = (x1 EQ 1 AND g1 EQ 2);
MISSING = ALL (999);
USEVAR = y11-y24 x4;
ANALYSIS:
TYPE = MEANSTRUCTURE;
MODEL:
level1 BY y11-y14@1;
trend1 BY y11@0 y12@1 y13*2.5 y14*3.5;
level2 BY y21-y24@1;
trend2 BY y21@0 y22@1 y23@2 y24@3;
[y11-y24@0];
[level1 trend1 level2 trend2];
level1-trend2 ON x4;
! the statement above lets all 4 growth factors be
! regressed on the covariate,
! while the statement below adds that the trend
! growth factors are also regressed on the level
! growth factor of the other process
trend1 ON level2;
trend2 ON level1;
level1 WITH level2;
! the statement above correlates the residuals of the
! level growth factors, while the residuals of the
! trend growth factors are correlated by default given
! that the trend growth factors do not influence other
! latent variables
y21 WITH y22;
! the statement above correlates the residuals of the
! second growth process' first two outcomes
OUTPUT: STANDARDIZED;
*** WARNING in ANALYSIS command
Starting with Version 5, TYPE=MEANSTRUCTURE is the default for all
analyses. To remove means from the model, use
MODEL=NOMEANSTRUCTURE in the ANALYSIS command.
*** WARNING
Data set contains cases with missing on all variables.
These cases were not included in the analysis.
Number of cases with missing on all variables: 1
2 WARNING(S) FOUND IN THE INPUT INSTRUCTIONS
cont5
Growth model for two parallel processes observed over
four time points. Regression of the slope in one process
on the intercept of the other and a covariate.
Non-linear growth for the first process by estimating the
last two timesteps. Linear growth for the second process.
First process: y11, y12, y13, y14
Second process: y21, y22, y23, y24
For related work, see:
Muthen, B. (1997). Latent variable modeling with longitudinal
and and multilevel data. In A. Raftery (ed.), Sociological
Methodology 1997 (pp. 453-480). Boston: Blackwell Publishers.
SUMMARY OF ANALYSIS
Number of groups 1
Number of observations 1489
Number of dependent variables 8
Number of independent variables 1
Number of continuous latent variables 4
Observed dependent variables
Continuous
Y11 Y12 Y13 Y14 Y21 Y22
Y23 Y24
Observed independent variables
X4
Continuous latent variables
LEVEL1 TREND1 LEVEL2 TREND2
Estimator ML
Information matrix OBSERVED
Maximum number of iterations 1000
Convergence criterion 0.500D-04
Maximum number of steepest descent iterations 20
Maximum number of iterations for H1 2000
Convergence criterion for H1 0.100D-03
Input data file(s)
comp.dat
Input data format
(3F8 F8.4 8F8.2 3F8 2F8.2)
SUMMARY OF DATA
Number of missing data patterns 53
COVARIANCE COVERAGE OF DATA
Minimum covariance coverage value 0.100
PROPORTION OF DATA PRESENT
Covariance Coverage
Y11 Y12 Y13 Y14 Y21
________ ________ ________ ________ ________
Y11 0.985
Y12 0.877 0.886
Y13 0.786 0.754 0.795
Y14 0.740 0.704 0.699 0.747
Y21 0.978 0.877 0.787 0.739 0.990
Y22 0.861 0.864 0.741 0.691 0.864
Y23 0.775 0.743 0.766 0.688 0.776
Y24 0.754 0.717 0.707 0.733 0.754
X4 0.981 0.885 0.794 0.745 0.986
Covariance Coverage
Y22 Y23 Y24 X4
________ ________ ________ ________
Y22 0.872
Y23 0.729 0.784
Y24 0.704 0.699 0.761
X4 0.872 0.783 0.760 0.996
THE MODEL ESTIMATION TERMINATED NORMALLY
TESTS OF MODEL FIT
Chi-Square Test of Model Fit
Value 60.947
Degrees of Freedom 25
P-Value 0.0001
Chi-Square Test of Model Fit for the Baseline Model
Value 6236.410
Degrees of Freedom 36
P-Value 0.0000
CFI/TLI
CFI 0.994
TLI 0.992
Loglikelihood
H0 Value -29888.506
H1 Value -29858.032
Information Criteria
Number of Free Parameters 27
Akaike (AIC) 59831.011
Bayesian (BIC) 59974.269
Sample-Size Adjusted BIC 59888.498
(n* = (n + 2) / 24)
RMSEA (Root Mean Square Error Of Approximation)
Estimate 0.031
90 Percent C.I. 0.021 0.041
Probability RMSEA <= .05 0.999
SRMR (Standardized Root Mean Square Residual)
Value 0.035
MODEL RESULTS
Two-Tailed
Estimate S.E. Est./S.E. P-Value
LEVEL1 BY
Y11 1.000 0.000 999.000 999.000
Y12 1.000 0.000 999.000 999.000
Y13 1.000 0.000 999.000 999.000
Y14 1.000 0.000 999.000 999.000
TREND1 BY
Y11 0.000 0.000 999.000 999.000
Y12 1.000 0.000 999.000 999.000
Y13 2.328 0.109 21.430 0.000
Y14 3.368 0.163 20.675 0.000
LEVEL2 BY
Y21 1.000 0.000 999.000 999.000
Y22 1.000 0.000 999.000 999.000
Y23 1.000 0.000 999.000 999.000
Y24 1.000 0.000 999.000 999.000
TREND2 BY
Y21 0.000 0.000 999.000 999.000
Y22 1.000 0.000 999.000 999.000
Y23 2.000 0.000 999.000 999.000
Y24 3.000 0.000 999.000 999.000
TREND1 ON
LEVEL2 0.218 0.051 4.272 0.000
TREND2 ON
LEVEL1 -0.009 0.004 -2.384 0.017
LEVEL1 ON
X4 3.965 0.314 12.629 0.000
TREND1 ON
X4 0.325 0.089 3.659 0.000
LEVEL2 ON
X4 0.189 0.092 2.064 0.039
TREND2 ON
X4 0.023 0.043 0.522 0.602
LEVEL1 WITH
LEVEL2 5.300 0.602 8.810 0.000
TREND2 WITH
TREND1 0.083 0.067 1.235 0.217
Y21 WITH
Y22 0.943 0.173 5.450 0.000
Intercepts
Y11 0.000 0.000 999.000 999.000
Y12 0.000 0.000 999.000 999.000
Y13 0.000 0.000 999.000 999.000
Y14 0.000 0.000 999.000 999.000
Y21 0.000 0.000 999.000 999.000
Y22 0.000 0.000 999.000 999.000
Y23 0.000 0.000 999.000 999.000
Y24 0.000 0.000 999.000 999.000
LEVEL1 51.627 0.233 221.225 0.000
TREND1 0.169 0.567 0.299 0.765
LEVEL2 11.140 0.067 165.750 0.000
TREND2 0.173 0.204 0.850 0.396
Residual Variances
Y11 11.952 0.991 12.063 0.000
Y12 13.885 0.832 16.678 0.000
Y13 16.624 1.079 15.403 0.000
Y14 26.531 1.908 13.903 0.000
Y21 4.220 0.232 18.190 0.000
Y22 3.700 0.203 18.193 0.000
Y23 3.409 0.194 17.597 0.000
Y24 3.365 0.268 12.545 0.000
LEVEL1 68.700 2.808 24.466 0.000
TREND1 1.842 0.296 6.219 0.000
LEVEL2 3.163 0.208 15.236 0.000
TREND2 0.263 0.038 6.871 0.000
STANDARDIZED MODEL RESULTS
STDYX Standardization
Two-Tailed
Estimate S.E. Est./S.E. P-Value
LEVEL1 BY
Y11 0.931 0.006 156.546 0.000
Y12 0.892 0.006 138.466 0.000
Y13 0.823 0.010 84.354 0.000
Y14 0.742 0.012 63.797 0.000
TREND1 BY
Y11 0.000 0.000 999.000 999.000
Y12 0.146 0.011 13.572 0.000
Y13 0.313 0.017 18.240 0.000
Y14 0.409 0.021 19.570 0.000
LEVEL2 BY
Y21 0.656 0.018 36.789 0.000
Y22 0.672 0.018 37.012 0.000
Y23 0.653 0.019 34.620 0.000
Y24 0.606 0.019 31.665 0.000
TREND2 BY
Y21 0.000 0.000 999.000 999.000
Y22 0.195 0.014 13.496 0.000
Y23 0.380 0.026 14.632 0.000
Y24 0.528 0.035 15.237 0.000
TREND1 ON
LEVEL2 0.271 0.063 4.334 0.000
TREND2 ON
LEVEL1 -0.156 0.065 -2.409 0.016
LEVEL1 ON
X4 0.332 0.025 13.411 0.000
TREND1 ON
X4 0.166 0.045 3.727 0.000
LEVEL2 ON
X4 0.078 0.038 2.069 0.038
TREND2 ON
X4 0.032 0.062 0.522 0.602
LEVEL1 WITH
LEVEL2 0.360 0.036 9.973 0.000
TREND2 WITH
TREND1 0.120 0.094 1.271 0.204
Y21 WITH
Y22 0.239 0.037 6.480 0.000
Intercepts
Y11 0.000 0.000 999.000 999.000
Y12 0.000 0.000 999.000 999.000
Y13 0.000 0.000 999.000 999.000
Y14 0.000 0.000 999.000 999.000
Y21 0.000 0.000 999.000 999.000
Y22 0.000 0.000 999.000 999.000
Y23 0.000 0.000 999.000 999.000
Y24 0.000 0.000 999.000 999.000
LEVEL1 5.876 0.122 48.135 0.000
TREND1 0.118 0.395 0.298 0.765
LEVEL2 6.244 0.208 30.034 0.000
TREND2 0.334 0.390 0.855 0.392
Residual Variances
Y11 0.134 0.011 12.119 0.000
Y12 0.143 0.009 16.513 0.000
Y13 0.146 0.010 14.906 0.000
Y14 0.189 0.014 13.503 0.000
Y21 0.570 0.023 24.391 0.000
Y22 0.525 0.022 23.916 0.000
Y23 0.457 0.020 22.906 0.000
Y24 0.388 0.029 13.460 0.000
LEVEL1 0.890 0.016 54.242 0.000
TREND1 0.892 0.038 23.536 0.000
LEVEL2 0.994 0.006 169.127 0.000
TREND2 0.978 0.018 53.458 0.000
STDY Standardization
Two-Tailed
Estimate S.E. Est./S.E. P-Value
LEVEL1 BY
Y11 0.931 0.006 156.546 0.000
Y12 0.892 0.006 138.466 0.000
Y13 0.823 0.010 84.354 0.000
Y14 0.742 0.012 63.797 0.000
TREND1 BY
Y11 0.000 0.000 999.000 999.000
Y12 0.146 0.011 13.572 0.000
Y13 0.313 0.017 18.240 0.000
Y14 0.409 0.021 19.570 0.000
LEVEL2 BY
Y21 0.656 0.018 36.789 0.000
Y22 0.672 0.018 37.012 0.000
Y23 0.653 0.019 34.620 0.000
Y24 0.606 0.019 31.665 0.000
TREND2 BY
Y21 0.000 0.000 999.000 999.000
Y22 0.195 0.014 13.496 0.000
Y23 0.380 0.026 14.632 0.000
Y24 0.528 0.035 15.237 0.000
TREND1 ON
LEVEL2 0.271 0.063 4.334 0.000
TREND2 ON
LEVEL1 -0.156 0.065 -2.409 0.016
LEVEL1 ON
X4 0.451 0.033 13.740 0.000
TREND1 ON
X4 0.226 0.061 3.735 0.000
LEVEL2 ON
X4 0.106 0.051 2.071 0.038
TREND2 ON
X4 0.044 0.084 0.522 0.602
LEVEL1 WITH
LEVEL2 0.360 0.036 9.973 0.000
TREND2 WITH
TREND1 0.120 0.094 1.271 0.204
Y21 WITH
Y22 0.239 0.037 6.480 0.000
Intercepts
Y11 0.000 0.000 999.000 999.000
Y12 0.000 0.000 999.000 999.000
Y13 0.000 0.000 999.000 999.000
Y14 0.000 0.000 999.000 999.000
Y21 0.000 0.000 999.000 999.000
Y22 0.000 0.000 999.000 999.000
Y23 0.000 0.000 999.000 999.000
Y24 0.000 0.000 999.000 999.000
LEVEL1 5.876 0.122 48.135 0.000
TREND1 0.118 0.395 0.298 0.765
LEVEL2 6.244 0.208 30.034 0.000
TREND2 0.334 0.390 0.855 0.392
Residual Variances
Y11 0.134 0.011 12.119 0.000
Y12 0.143 0.009 16.513 0.000
Y13 0.146 0.010 14.906 0.000
Y14 0.189 0.014 13.503 0.000
Y21 0.570 0.023 24.391 0.000
Y22 0.525 0.022 23.916 0.000
Y23 0.457 0.020 22.906 0.000
Y24 0.388 0.029 13.460 0.000
LEVEL1 0.890 0.016 54.242 0.000
TREND1 0.892 0.038 23.536 0.000
LEVEL2 0.994 0.006 169.127 0.000
TREND2 0.978 0.018 53.458 0.000
STD Standardization
Two-Tailed
Estimate S.E. Est./S.E. P-Value
LEVEL1 BY
Y11 8.786 0.178 49.312 0.000
Y12 8.786 0.178 49.312 0.000
Y13 8.786 0.178 49.312 0.000
Y14 8.786 0.178 49.312 0.000
TREND1 BY
Y11 0.000 0.000 999.000 999.000
Y12 1.437 0.106 13.500 0.000
Y13 3.345 0.192 17.429 0.000
Y14 4.841 0.271 17.883 0.000
LEVEL2 BY
Y21 1.784 0.058 30.554 0.000
Y22 1.784 0.058 30.554 0.000
Y23 1.784 0.058 30.554 0.000
Y24 1.784 0.058 30.554 0.000
TREND2 BY
Y21 0.000 0.000 999.000 999.000
Y22 0.519 0.037 13.906 0.000
Y23 1.037 0.075 13.906 0.000
Y24 1.556 0.112 13.906 0.000
TREND1 ON
LEVEL2 0.271 0.063 4.334 0.000
TREND2 ON
LEVEL1 -0.156 0.065 -2.409 0.016
LEVEL1 ON
X4 0.451 0.033 13.740 0.000
TREND1 ON
X4 0.226 0.061 3.735 0.000
LEVEL2 ON
X4 0.106 0.051 2.071 0.038
TREND2 ON
X4 0.044 0.084 0.522 0.602
LEVEL1 WITH
LEVEL2 0.360 0.036 9.973 0.000
TREND2 WITH
TREND1 0.120 0.094 1.271 0.204
Y21 WITH
Y22 0.943 0.173 5.450 0.000
Intercepts
Y11 0.000 0.000 999.000 999.000
Y12 0.000 0.000 999.000 999.000
Y13 0.000 0.000 999.000 999.000
Y14 0.000 0.000 999.000 999.000
Y21 0.000 0.000 999.000 999.000
Y22 0.000 0.000 999.000 999.000
Y23 0.000 0.000 999.000 999.000
Y24 0.000 0.000 999.000 999.000
LEVEL1 5.876 0.122 48.135 0.000
TREND1 0.118 0.395 0.298 0.765
LEVEL2 6.244 0.208 30.034 0.000
TREND2 0.334 0.390 0.855 0.392
Residual Variances
Y11 11.952 0.991 12.063 0.000
Y12 13.885 0.832 16.678 0.000
Y13 16.624 1.079 15.403 0.000
Y14 26.531 1.908 13.903 0.000
Y21 4.220 0.232 18.190 0.000
Y22 3.700 0.203 18.193 0.000
Y23 3.409 0.194 17.597 0.000
Y24 3.365 0.268 12.545 0.000
LEVEL1 0.890 0.016 54.242 0.000
TREND1 0.892 0.038 23.536 0.000
LEVEL2 0.994 0.006 169.127 0.000
TREND2 0.978 0.018 53.458 0.000
R-SQUARE
Observed Two-Tailed
Variable Estimate S.E. Est./S.E. P-Value
Y11 0.866 0.011 78.273 0.000
Y12 0.857 0.009 98.886 0.000
Y13 0.854 0.010 87.362 0.000
Y14 0.811 0.014 57.878 0.000
Y21 0.430 0.023 18.394 0.000
Y22 0.475 0.022 21.661 0.000
Y23 0.543 0.020 27.261 0.000
Y24 0.612 0.029 21.200 0.000
Latent Two-Tailed
Variable Estimate S.E. Est./S.E. P-Value
LEVEL1 0.110 0.016 6.706 0.000
TREND1 0.108 0.038 2.857 0.004
LEVEL2 0.006 0.006 1.035 0.301
TREND2 0.022 0.018 1.212 0.225
QUALITY OF NUMERICAL RESULTS
Condition Number for the Information Matrix 0.125E-03
(ratio of smallest to largest eigenvalue)
Beginning Time: 20:26:33
Ending Time: 20:26:33
Elapsed Time: 00:00:00
MUTHEN & MUTHEN
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Los Angeles, CA 90066
Tel: (310) 391-9971
Fax: (310) 391-8971
Web: www.StatModel.com
Support: Support@StatModel.com
Copyright (c) 1998-2008 Muthen & Muthen
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