Mplus VERSION 6
MUTHEN & MUTHEN
04/25/2010  10:58 PM

INPUT INSTRUCTIONS

  TITLE:
          penn1

         1-class no covariates

  DATA:  FILE IS lsay.dat;
         FORMAT is 3f8 f8.4 8f8.2 3f8 2f8.2;

  VARIABLE:  NAMES ARE cohort id school weight math7 math8 math9 math10
             att7 att8 att9 att10 gender mothed homeres;
             USEOBS = (gender EQ 1 AND cohort EQ 2);
             MISSING = ALL (999);
             USEVAR =  math7-math10 ;
             classes = c(1);

  ANALYSIS:  TYPE = mixture;
                  miterations = 5;

  MODEL:
          %overall%
          intercpt BY math7-math10 @1;
          slope BY math8@1 math9@2.5 math10@3.5;
          [math7-math10@0];

          math7-math9*7 math10*13;
          intercpt*64.5 slope*1.3;
          slope with intercpt*3.1;

          %c#1%
          [intercpt*42.8 slope*.6];

  OUTPUT:  TECH1 tech8;



*** 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:  8
   1 WARNING(S) FOUND IN THE INPUT INSTRUCTIONS




penn1

1-class no covariates

SUMMARY OF ANALYSIS

Number of groups                                                 1
Number of observations                                        1482

Number of dependent variables                                    4
Number of independent variables                                  0
Number of continuous latent variables                            2
Number of categorical latent variables                           1

Observed dependent variables

  Continuous
   MATH7       MATH8       MATH9       MATH10

Continuous latent variables
   INTERCPT    SLOPE

Categorical latent variables
   C


Estimator                                                      MLR
Information matrix                                        OBSERVED
Optimization Specifications for the Quasi-Newton Algorithm for
Continuous Outcomes
  Maximum number of iterations                                 100
  Convergence criterion                                  0.100D-05
Optimization Specifications for the EM Algorithm
  Maximum number of iterations                                   5
  Convergence criteria
    Loglikelihood change                                 0.100D-06
    Relative loglikelihood change                        0.100D-06
    Derivative                                           0.100D-05
Optimization Specifications for the M step of the EM Algorithm for
Categorical Latent variables
  Number of M step iterations                                    1
  M step convergence criterion                           0.100D-05
  Basis for M step termination                           ITERATION
Optimization Specifications for the M step of the EM Algorithm for
Censored, Binary or Ordered Categorical (Ordinal), Unordered
Categorical (Nominal) and Count Outcomes
  Number of M step iterations                                    1
  M step convergence criterion                           0.100D-05
  Basis for M step termination                           ITERATION
  Maximum value for logit thresholds                            15
  Minimum value for logit thresholds                           -15
  Minimum expected cell size for chi-square              0.100D-01
Maximum number of iterations for H1                           2000
Convergence criterion for H1                             0.100D-03
Optimization algorithm                                         EMA

Input data file(s)
  lsay.dat
Input data format
  (3F8 F8.4 8F8.2 3F8 2F8.2)


SUMMARY OF DATA

     Number of missing data patterns            13
     Number of y missing data patterns          13
     Number of u missing data patterns           0


COVARIANCE COVERAGE OF DATA

Minimum covariance coverage value   0.100


     PROPORTION OF DATA PRESENT FOR Y


           Covariance Coverage
              MATH7         MATH8         MATH9         MATH10
              ________      ________      ________      ________
 MATH7          0.990
 MATH8          0.881         0.890
 MATH9          0.790         0.758         0.799
 MATH10         0.744         0.708         0.702         0.750



THE MODEL ESTIMATION TERMINATED NORMALLY



TESTS OF MODEL FIT

Loglikelihood

          H0 Value                      -16741.551
          H0 Scaling Correction Factor       1.364
            for MLR

Information Criteria

          Number of Free Parameters              9
          Akaike (AIC)                   33501.101
          Bayesian (BIC)                 33548.812
          Sample-Size Adjusted BIC       33520.221
            (n* = (n + 2) / 24)



FINAL CLASS COUNTS AND PROPORTIONS FOR THE LATENT CLASSES
BASED ON THE ESTIMATED MODEL

    Latent
   Classes

       1       1482.00000          1.00000


FINAL CLASS COUNTS AND PROPORTIONS FOR THE LATENT CLASS PATTERNS
BASED ON ESTIMATED POSTERIOR PROBABILITIES

    Latent
   Classes

       1       1482.00000          1.00000


CLASSIFICATION OF INDIVIDUALS BASED ON THEIR MOST LIKELY LATENT CLASS MEMBERSHIP

Class Counts and Proportions

    Latent
   Classes

       1             1482          1.00000


Average Latent Class Probabilities for Most Likely Latent Class Membership (Row)
by Latent Class (Column)

           1

    1   1.000


MODEL RESULTS

                                                    Two-Tailed
                    Estimate       S.E.  Est./S.E.    P-Value

Latent Class 1

 INTERCPT BY
    MATH7              1.000      0.000    999.000    999.000
    MATH8              1.000      0.000    999.000    999.000
    MATH9              1.000      0.000    999.000    999.000
    MATH10             1.000      0.000    999.000    999.000

 SLOPE    BY
    MATH8              1.000      0.000    999.000    999.000
    MATH9              2.500      0.000    999.000    999.000
    MATH10             3.500      0.000    999.000    999.000

 SLOPE    WITH
    INTERCPT           3.686      0.761      4.841      0.000

 Means
    INTERCPT          51.668      0.239    216.181      0.000
    SLOPE              2.464      0.060     40.726      0.000

 Intercepts
    MATH7              0.000      0.000    999.000    999.000
    MATH8              0.000      0.000    999.000    999.000
    MATH9              0.000      0.000    999.000    999.000
    MATH10             0.000      0.000    999.000    999.000

 Variances
    INTERCPT          73.916      3.026     24.425      0.000
    SLOPE              1.518      0.278      5.452      0.000

 Residual Variances
    MATH7             13.824      1.317     10.492      0.000
    MATH8             13.461      1.058     12.723      0.000
    MATH9             16.148      1.258     12.833      0.000
    MATH10            28.345      2.884      9.829      0.000


QUALITY OF NUMERICAL RESULTS

     Condition Number for the Information Matrix              0.724E-03
       (ratio of smallest to largest eigenvalue)


TECHNICAL 1 OUTPUT


     PARAMETER SPECIFICATION FOR LATENT CLASS 1


           NU
              MATH7         MATH8         MATH9         MATH10
              ________      ________      ________      ________
 1                  0             0             0             0


           LAMBDA
              INTERCPT      SLOPE
              ________      ________
 MATH7              0             0
 MATH8              0             0
 MATH9              0             0
 MATH10             0             0


           THETA
              MATH7         MATH8         MATH9         MATH10
              ________      ________      ________      ________
 MATH7              1
 MATH8              0             2
 MATH9              0             0             3
 MATH10             0             0             0             4


           ALPHA
              INTERCPT      SLOPE
              ________      ________
 1                  5             6


           BETA
              INTERCPT      SLOPE
              ________      ________
 INTERCPT           0             0
 SLOPE              0             0


           PSI
              INTERCPT      SLOPE
              ________      ________
 INTERCPT           7
 SLOPE              8             9


     PARAMETER SPECIFICATION FOR LATENT CLASS REGRESSION MODEL PART


           ALPHA(C)
              C#1
              ________
 1                  0


     STARTING VALUES FOR LATENT CLASS 1


           NU
              MATH7         MATH8         MATH9         MATH10
              ________      ________      ________      ________
 1              0.000         0.000         0.000         0.000


           LAMBDA
              INTERCPT      SLOPE
              ________      ________
 MATH7          1.000         0.000
 MATH8          1.000         1.000
 MATH9          1.000         2.500
 MATH10         1.000         3.500


           THETA
              MATH7         MATH8         MATH9         MATH10
              ________      ________      ________      ________
 MATH7          7.000
 MATH8          0.000         7.000
 MATH9          0.000         0.000         7.000
 MATH10         0.000         0.000         0.000        13.000


           ALPHA
              INTERCPT      SLOPE
              ________      ________
 1             42.800         0.600


           BETA
              INTERCPT      SLOPE
              ________      ________
 INTERCPT       0.000         0.000
 SLOPE          0.000         0.000


           PSI
              INTERCPT      SLOPE
              ________      ________
 INTERCPT      64.500
 SLOPE          3.100         1.300


     STARTING VALUES FOR LATENT CLASS REGRESSION MODEL PART


           ALPHA(C)
              C#1
              ________
 1              0.000


TECHNICAL 8 OUTPUT


  ITER  LOGLIKELIHOOD    ABS CHANGE   REL CHANGE      CLASS COUNTS    ALGORITHM
     1 -0.18821421D+05    0.0000000    0.0000000   1482.000              EM
     2 -0.16741551D+05 2079.8705490    0.1105055   1482.000              EM
     3 -0.16741551D+05    0.0000000    0.0000000   1482.000              EM


     Beginning Time:  22:58:14
        Ending Time:  22:58:15
       Elapsed Time:  00:00:01



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