Mplus VERSION 6
MUTHEN & MUTHEN
04/25/2010 10:58 PM
INPUT INSTRUCTIONS
TITLE:
cont8
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
Complex sample analysis (aggregated or marginal model),
taking clustering (non-independence of observations) into
account. Compare results with wGrowthp1 (same growth model).
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;
CLUSTER = cluster;
ANALYSIS:
TYPE = MEANSTRUCTURE COMPLEX;
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 in VARIABLE command
When a subpopulation is analyzed with TYPE=COMPLEX, standard errors
may be incorrect. Use the SUBPOPULATION option instead of the
USEOBSERVATIONS option to obtain correct standard errors.
*** 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
*** WARNING
Data set contains cases with missing on x-variables.
These cases were not included in the analysis.
Number of cases with missing on x-variables: 6
*** WARNING
Data set contains cases with missing on all variables except
x-variables. These cases were not included in the analysis.
Number of cases with missing on all variables except x-variables: 1
5 WARNING(S) FOUND IN THE INPUT INSTRUCTIONS
cont8
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
Complex sample analysis (aggregated or marginal model),
taking clustering (non-independence of observations) into
account. Compare results with wGrowthp1 (same growth model).
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 1482
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
Variables with special functions
Cluster variable CLUSTER
Estimator MLR
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 49
Number of clusters 52
COVARIANCE COVERAGE OF DATA
Minimum covariance coverage value 0.100
PROPORTION OF DATA PRESENT
Covariance Coverage
Y11 Y12 Y13 Y14 Y21
________ ________ ________ ________ ________
Y11 0.986
Y12 0.881 0.889
Y13 0.789 0.757 0.798
Y14 0.742 0.707 0.702 0.749
Y21 0.978 0.881 0.790 0.742 0.991
Y22 0.865 0.868 0.744 0.694 0.868
Y23 0.778 0.746 0.769 0.690 0.779
Y24 0.756 0.720 0.710 0.735 0.756
X4 0.986 0.889 0.798 0.749 0.991
Covariance Coverage
Y22 Y23 Y24 X4
________ ________ ________ ________
Y22 0.876
Y23 0.732 0.787
Y24 0.707 0.702 0.763
X4 0.876 0.787 0.763 1.000
THE MODEL ESTIMATION TERMINATED NORMALLY
TESTS OF MODEL FIT
Chi-Square Test of Model Fit
Value 48.120*
Degrees of Freedom 25
P-Value 0.0036
Scaling Correction Factor 1.268
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.
Chi-Square Test of Model Fit for the Baseline Model
Value 5391.440
Degrees of Freedom 36
P-Value 0.0000
CFI/TLI
CFI 0.996
TLI 0.994
Loglikelihood
H0 Value -29827.812
H0 Scaling Correction Factor 1.901
for MLR
H1 Value -29797.315
H1 Scaling Correction Factor 1.597
for MLR
Information Criteria
Number of Free Parameters 27
Akaike (AIC) 59709.624
Bayesian (BIC) 59852.755
Sample-Size Adjusted BIC 59766.984
(n* = (n + 2) / 24)
RMSEA (Root Mean Square Error Of Approximation)
Estimate 0.025
90 Percent C.I. 0.014 0.036
Probability RMSEA <= .05 1.000
SRMR (Standardized Root Mean Square Residual)
Value 0.036
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 2.331 0.186 12.532 0.000
Y14 3.378 0.295 11.451 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 0.221 0.068 3.258 0.001
TREND2 ON
LEVEL1 -0.009 0.003 -2.519 0.012
LEVEL1 ON
X4 3.963 0.476 8.332 0.000
TREND1 ON
X4 0.326 0.109 2.988 0.003
LEVEL2 ON
X4 0.185 0.108 1.708 0.088
TREND2 ON
X4 0.022 0.053 0.416 0.678
LEVEL1 WITH
LEVEL2 5.213 0.734 7.100 0.000
TREND2 WITH
TREND1 0.077 0.083 0.932 0.351
Y21 WITH
Y22 0.940 0.218 4.315 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 51.664 0.537 96.208 0.000
TREND1 0.129 0.683 0.189 0.850
LEVEL2 11.147 0.104 107.656 0.000
TREND2 0.146 0.186 0.788 0.431
Residual Variances
Y11 11.909 1.528 7.795 0.000
Y12 13.902 1.291 10.772 0.000
Y13 16.623 1.525 10.903 0.000
Y14 26.400 3.511 7.520 0.000
Y21 4.212 0.259 16.247 0.000
Y22 3.699 0.239 15.473 0.000
Y23 3.412 0.203 16.836 0.000
Y24 3.365 0.311 10.820 0.000
LEVEL1 68.630 3.065 22.389 0.000
TREND1 1.836 0.461 3.984 0.000
LEVEL2 3.147 0.265 11.892 0.000
TREND2 0.263 0.041 6.424 0.000
STANDARDIZED MODEL RESULTS
STDYX Standardization
Two-Tailed
Estimate S.E. Est./S.E. P-Value
LEVEL1 BY
Y11 0.931 0.009 108.281 0.000
Y12 0.892 0.009 95.874 0.000
Y13 0.823 0.012 66.785 0.000
Y14 0.742 0.016 46.464 0.000
TREND1 BY
Y11 0.000 0.000 999.000 999.000
Y12 0.146 0.017 8.680 0.000
Y13 0.314 0.021 15.102 0.000
Y14 0.410 0.025 16.643 0.000
LEVEL2 BY
Y21 0.655 0.023 28.852 0.000
Y22 0.671 0.023 29.541 0.000
Y23 0.651 0.023 27.780 0.000
Y24 0.604 0.023 25.793 0.000
TREND2 BY
Y21 0.000 0.000 999.000 999.000
Y22 0.195 0.016 12.184 0.000
Y23 0.379 0.028 13.398 0.000
Y24 0.528 0.038 13.846 0.000
TREND1 ON
LEVEL2 0.274 0.073 3.748 0.000
TREND2 ON
LEVEL1 -0.148 0.059 -2.496 0.013
LEVEL1 ON
X4 0.332 0.036 9.258 0.000
TREND1 ON
X4 0.167 0.052 3.225 0.001
LEVEL2 ON
X4 0.076 0.044 1.738 0.082
TREND2 ON
X4 0.031 0.075 0.415 0.678
LEVEL1 WITH
LEVEL2 0.355 0.044 8.150 0.000
TREND2 WITH
TREND1 0.111 0.120 0.931 0.352
Y21 WITH
Y22 0.238 0.049 4.833 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 5.883 0.166 35.460 0.000
TREND1 0.090 0.478 0.188 0.851
LEVEL2 6.266 0.281 22.319 0.000
TREND2 0.282 0.357 0.790 0.429
Residual Variances
Y11 0.134 0.016 8.361 0.000
Y12 0.143 0.012 12.358 0.000
Y13 0.146 0.013 11.102 0.000
Y14 0.188 0.023 8.085 0.000
Y21 0.571 0.030 19.200 0.000
Y22 0.525 0.028 19.079 0.000
Y23 0.457 0.021 21.826 0.000
Y24 0.388 0.031 12.414 0.000
LEVEL1 0.890 0.024 37.436 0.000
TREND1 0.890 0.050 17.871 0.000
LEVEL2 0.994 0.007 147.713 0.000
TREND2 0.980 0.015 63.704 0.000
STDY Standardization
Two-Tailed
Estimate S.E. Est./S.E. P-Value
LEVEL1 BY
Y11 0.931 0.009 108.281 0.000
Y12 0.892 0.009 95.874 0.000
Y13 0.823 0.012 66.785 0.000
Y14 0.742 0.016 46.464 0.000
TREND1 BY
Y11 0.000 0.000 999.000 999.000
Y12 0.146 0.017 8.680 0.000
Y13 0.314 0.021 15.102 0.000
Y14 0.410 0.025 16.643 0.000
LEVEL2 BY
Y21 0.655 0.023 28.852 0.000
Y22 0.671 0.023 29.541 0.000
Y23 0.651 0.023 27.780 0.000
Y24 0.604 0.023 25.793 0.000
TREND2 BY
Y21 0.000 0.000 999.000 999.000
Y22 0.195 0.016 12.184 0.000
Y23 0.379 0.028 13.398 0.000
Y24 0.528 0.038 13.846 0.000
TREND1 ON
LEVEL2 0.274 0.073 3.748 0.000
TREND2 ON
LEVEL1 -0.148 0.059 -2.496 0.013
LEVEL1 ON
X4 0.451 0.048 9.417 0.000
TREND1 ON
X4 0.227 0.070 3.233 0.001
LEVEL2 ON
X4 0.104 0.060 1.739 0.082
TREND2 ON
X4 0.043 0.103 0.415 0.678
LEVEL1 WITH
LEVEL2 0.355 0.044 8.150 0.000
TREND2 WITH
TREND1 0.111 0.120 0.931 0.352
Y21 WITH
Y22 0.238 0.049 4.833 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 5.883 0.166 35.460 0.000
TREND1 0.090 0.478 0.188 0.851
LEVEL2 6.266 0.281 22.319 0.000
TREND2 0.282 0.357 0.790 0.429
Residual Variances
Y11 0.134 0.016 8.361 0.000
Y12 0.143 0.012 12.358 0.000
Y13 0.146 0.013 11.102 0.000
Y14 0.188 0.023 8.085 0.000
Y21 0.571 0.030 19.200 0.000
Y22 0.525 0.028 19.079 0.000
Y23 0.457 0.021 21.826 0.000
Y24 0.388 0.031 12.414 0.000
LEVEL1 0.890 0.024 37.436 0.000
TREND1 0.890 0.050 17.871 0.000
LEVEL2 0.994 0.007 147.713 0.000
TREND2 0.980 0.015 63.704 0.000
STD Standardization
Two-Tailed
Estimate S.E. Est./S.E. P-Value
LEVEL1 BY
Y11 8.782 0.222 39.484 0.000
Y12 8.782 0.222 39.484 0.000
Y13 8.782 0.222 39.484 0.000
Y14 8.782 0.222 39.484 0.000
TREND1 BY
Y11 0.000 0.000 999.000 999.000
Y12 1.436 0.176 8.142 0.000
Y13 3.348 0.243 13.802 0.000
Y14 4.851 0.324 14.964 0.000
LEVEL2 BY
Y21 1.779 0.076 23.467 0.000
Y22 1.779 0.076 23.467 0.000
Y23 1.779 0.076 23.467 0.000
Y24 1.779 0.076 23.467 0.000
TREND2 BY
Y21 0.000 0.000 999.000 999.000
Y22 0.518 0.040 13.073 0.000
Y23 1.037 0.079 13.073 0.000
Y24 1.555 0.119 13.073 0.000
TREND1 ON
LEVEL2 0.274 0.073 3.748 0.000
TREND2 ON
LEVEL1 -0.148 0.059 -2.496 0.013
LEVEL1 ON
X4 0.451 0.048 9.417 0.000
TREND1 ON
X4 0.227 0.070 3.233 0.001
LEVEL2 ON
X4 0.104 0.060 1.739 0.082
TREND2 ON
X4 0.043 0.103 0.415 0.678
LEVEL1 WITH
LEVEL2 0.355 0.044 8.150 0.000
TREND2 WITH
TREND1 0.111 0.120 0.931 0.352
Y21 WITH
Y22 0.940 0.218 4.315 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 5.883 0.166 35.460 0.000
TREND1 0.090 0.478 0.188 0.851
LEVEL2 6.266 0.281 22.319 0.000
TREND2 0.282 0.357 0.790 0.429
Residual Variances
Y11 11.909 1.528 7.795 0.000
Y12 13.902 1.291 10.772 0.000
Y13 16.623 1.525 10.903 0.000
Y14 26.400 3.511 7.520 0.000
Y21 4.212 0.259 16.247 0.000
Y22 3.699 0.239 15.473 0.000
Y23 3.412 0.203 16.836 0.000
Y24 3.365 0.311 10.820 0.000
LEVEL1 0.890 0.024 37.436 0.000
TREND1 0.890 0.050 17.871 0.000
LEVEL2 0.994 0.007 147.713 0.000
TREND2 0.980 0.015 63.704 0.000
R-SQUARE
Observed Two-Tailed
Variable Estimate S.E. Est./S.E. P-Value
Y11 0.866 0.016 54.140 0.000
Y12 0.857 0.012 73.828 0.000
Y13 0.854 0.013 65.027 0.000
Y14 0.812 0.023 34.831 0.000
Y21 0.429 0.030 14.426 0.000
Y22 0.475 0.028 17.228 0.000
Y23 0.543 0.021 25.919 0.000
Y24 0.612 0.031 19.556 0.000
Latent Two-Tailed
Variable Estimate S.E. Est./S.E. P-Value
LEVEL1 0.110 0.024 4.629 0.000
TREND1 0.110 0.050 2.204 0.028
LEVEL2 0.006 0.007 0.869 0.385
TREND2 0.020 0.015 1.296 0.195
QUALITY OF NUMERICAL RESULTS
Condition Number for the Information Matrix 0.661E-06
(ratio of smallest to largest eigenvalue)
Beginning Time: 22:58:09
Ending Time: 22:58:09
Elapsed Time: 00:00:00
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Los Angeles, CA 90066
Tel: (310) 391-9971
Fax: (310) 391-8971
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Copyright (c) 1998-2010 Muthen & Muthen
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