Mplus VERSION 7.4
MUTHEN & MUTHEN
11/09/2015 5:40 PM
INPUT INSTRUCTIONS
TITLE: Regressing math10 on math7
DATA:
FILE = dropout.dat;
FORMAT = 11f8 6f8.2 1f8 2f8.2 10f2;
VARIABLE:
NAMES ARE id school gender mothed fathed fathsei ethnic expect
pacpush pmpush homeres
math7 math8 math9 math10 math11 math12 problem esteem mathatt
clocatn dlocatn elocatn flocatn glocatn hlocatn ilocatn jlocatn
klocatn llocatn;
MISSING = mothed (8) fathed (8) fathsei (996 998)
ethnic (8) homeres (98) math7-math12 (996 998);
IDVARIABLE = id;
USEVAR = math7 math10;
Analysis:
type = random;
MODEL:
s | math10 ON math7;
s with math10 (cov);
math10 (resvary);
s (vbeta);
OUTPUT:
TECH1 SAMPSTAT STDYX RESIDUAL CINTERVAL;
Plot:
TYPE = PLOT3;
Model constraint:
plot (vygivenx);
loop(x,25,90,1);
vygivenx = vbeta*x*x + 2*cov*x + resvary;
*** WARNING in OUTPUT command
STANDARDIZED (STD, STDY, STDYX) options for TYPE=RANDOM require ALGORITHM=INTEGRATION.
Request for STANDARDIZED (STD, STDY, STDYX) is ignored.
*** WARNING in OUTPUT command
RESIDUAL option for TYPE=RANDOM requires ALGORITHM=INTEGRATION.
Request for RESIDUAL is ignored.
*** 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: 30
*** 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: 21
*** 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: 1046
5 WARNING(S) FOUND IN THE INPUT INSTRUCTIONS
Regressing math10 on math7
SUMMARY OF ANALYSIS
Number of groups 1
Number of observations 2019
Number of dependent variables 1
Number of independent variables 1
Number of continuous latent variables 1
Observed dependent variables
Continuous
MATH10
Observed independent variables
MATH7
Continuous latent variables
S
Variables with special functions
ID variable ID
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)
dropout.dat
Input data format
(11F8 6F8.2 1F8 2F8.2 10F2)
SUMMARY OF DATA
Number of missing data patterns 1
COVARIANCE COVERAGE OF DATA
Minimum covariance coverage value 0.100
PROPORTION OF DATA PRESENT
Covariance Coverage
MATH10 MATH7
________ ________
MATH10 1.000
MATH7 1.000 1.000
SAMPLE STATISTICS
ESTIMATED SAMPLE STATISTICS
Means
MATH10 MATH7
________ ________
1 63.624 51.515
Covariances
MATH10 MATH7
________ ________
MATH10 186.231
MATH7 109.098 103.212
Correlations
MATH10 MATH7
________ ________
MATH10 1.000
MATH7 0.787 1.000
MAXIMUM LOG-LIKELIHOOD VALUE FOR THE UNRESTRICTED (H1) MODEL IS -14712.416
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
MATH10 63.624 -0.321 29.600 0.05% 51.490 61.860 65.340
2019.000 186.231 -0.459 95.170 0.25% 68.400 75.290
MATH7 51.515 0.050 27.560 0.05% 42.080 48.670 51.810
2019.000 103.212 -0.621 85.020 0.05% 54.390 60.650
THE MODEL ESTIMATION TERMINATED NORMALLY
MODEL FIT INFORMATION
Number of Free Parameters 5
Loglikelihood
H0 Value -7078.820
H0 Scaling Correction Factor 1.1700
for MLR
Information Criteria
Akaike (AIC) 14167.639
Bayesian (BIC) 14195.691
Sample-Size Adjusted BIC 14179.806
(n* = (n + 2) / 24)
MODEL RESULTS
Two-Tailed
Estimate S.E. Est./S.E. P-Value
S WITH
MATH10 -4.413 0.841 -5.247 0.000
Means
S 1.053 0.017 61.778 0.000
Intercepts
MATH10 9.410 0.972 9.685 0.000
Variances
S 0.056 0.014 3.963 0.000
Residual Variances
MATH10 372.259 49.361 7.542 0.000
QUALITY OF NUMERICAL RESULTS
Condition Number for the Information Matrix 0.353E-09
(ratio of smallest to largest eigenvalue)
CONFIDENCE INTERVALS OF MODEL RESULTS
Lower .5% Lower 2.5% Lower 5% Estimate Upper 5% Upper 2.5% Upper .5%
S WITH
MATH10 -6.580 -6.062 -5.797 -4.413 -3.030 -2.765 -2.247
Means
S 1.009 1.019 1.025 1.053 1.081 1.086 1.097
Intercepts
MATH10 6.907 7.505 7.811 9.410 11.008 11.314 11.912
Variances
S 0.020 0.028 0.033 0.056 0.079 0.084 0.092
Residual Variances
MATH10 245.115 275.511 291.060 372.259 453.458 469.007 499.404
TECHNICAL 1 OUTPUT
PARAMETER SPECIFICATION
NU
MATH10 MATH7
________ ________
1 0 0
LAMBDA
S MATH10 MATH7
________ ________ ________
MATH10 0 0 0
MATH7 0 0 0
THETA
MATH10 MATH7
________ ________
MATH10 0
MATH7 0 0
ALPHA
S MATH10 MATH7
________ ________ ________
1 1 2 0
BETA
S MATH10 MATH7
________ ________ ________
S 0 0 0
MATH10 0 0 0
MATH7 0 0 0
PSI
S MATH10 MATH7
________ ________ ________
S 3
MATH10 4 5
MATH7 0 0 0
PARAMETER SPECIFICATION FOR THE ADDITIONAL PARAMETERS
NEW/ADDITIONAL PARAMETERS
VYGIVENX X
________ ________
1 6 7
STARTING VALUES
NU
MATH10 MATH7
________ ________
1 0.000 0.000
LAMBDA
S MATH10 MATH7
________ ________ ________
MATH10 0.000 1.000 0.000
MATH7 0.000 0.000 1.000
THETA
MATH10 MATH7
________ ________
MATH10 0.000
MATH7 0.000 0.000
ALPHA
S MATH10 MATH7
________ ________ ________
1 0.000 63.624 0.000
BETA
S MATH10 MATH7
________ ________ ________
S 0.000 0.000 0.000
MATH10 0.000 0.000 0.000
MATH7 0.000 0.000 0.000
PSI
S MATH10 MATH7
________ ________ ________
S 1.000
MATH10 0.000 93.115
MATH7 0.000 0.000 51.606
STARTING VALUES FOR THE ADDITIONAL PARAMETERS
NEW/ADDITIONAL PARAMETERS
VYGIVENX X
________ ________
1 0.500 0.000
SAMPLE STATISTICS FOR ESTIMATED FACTOR SCORES
SAMPLE STATISTICS
Means
S S_SE
________ ________
1 1.053 0.147
Covariances
S S_SE
________ ________
S 0.033
S_SE 0.000 0.001
Correlations
S S_SE
________ ________
S 1.000
S_SE -0.012 1.000
PLOT INFORMATION
The following plots are available:
Histograms (sample values, estimated factor scores)
Scatterplots (sample values, estimated factor scores)
Loop plots
Latent variable distribution plots
DIAGRAM INFORMATION
Use View Diagram under the Diagram menu in the Mplus Editor to view the diagram.
If running Mplus from the Mplus Diagrammer, the diagram opens automatically.
Diagram output
c:\users\bengt 2013\documents\bengt\mplus runs\a book - topic 1 mplus runs\regression\random coe
Beginning Time: 17:40:45
Ending Time: 17:40:51
Elapsed Time: 00:00:06
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