Mplus DEVELOPMENT MUTHEN & MUTHEN 02/21/2006 2:03 PM INPUT INSTRUCTIONS Title: lsay_3.inp LSAY math outcomes Growth mixture model with 3-classes DATA: file is lsay.dat; VARIABLE: Names are lsayid schcode classize urban tracking ntracks mthlvl female mthflg7-mthflg12 mothed fathed mothsei fathsei homeres race expect parapsh parcpsh parmpsh peerapsh peermpsh bas7 basse7 alg7 algse7 geo7 geose7 qlt7 qltse7 mth7 mthse7 mtha7 mthase7 bas8 basse8 alg8 algse8 geo8 geose8 qlt8 qltse8 mth8 mthse8 mtha8 mthase8 bas9 basse9 alg9 algse9 geo9 geose9 qlt9 qltse9 mth9 mthse9 mtha9 mthase9 bas10 basse10 alg10 algse10 geo10 geose10 qlt10 qltse10 mth10 mthse10 mtha10 mthase10 bas11 basse11 alg11 algse11 geo11 geose11 qlt11 qltse11 mth11 mthse11 mtha11 mthase11 bas12 basse12 alg12 algse12 geo12 geose12 qlt12 qltse12 mth12 mthse12 mtha12 mthase12 mthcrs7-mthcrs12 mtrk10-mtrk12 totstud lchfull lchpart parvis mcirr mclub strat mstrat comp mcomp african hispan asian expel arrest dropot self worth other satisf respect failure esteem problem cloctn dloctn eloctn floctn gloctn hloctn iloctn jloctn kloctn lloctn drink runawa suicid alc7 alc10 alc11 alc12 arest7 runa8 runa9 runa10 runa11 run12 suic8 suic9 suic10 suic11 suic12 drop7 drop8 drop9 drop10 drop11 drop12 fdrop8 fdrop9 fdrop10 fdrop11 fdrop12 enj7 good7 und7 useboy7 nerv7 wor7 scar7 use7 logic7 boybet7 job7 often7 enj8 good8 und8 useboy8 nerv8 wor8 scar8 use8 logic8 boybet8 job8 often8 enj9 good9 und9 useboy9 nerv9 wor9 scar9 use9 logic9 boybet9 job9 often9 enj10 good10 und10 useboy10 nerv10 wor10 scar10 use10 logic10 boybet10 job10 often10; Missing are all(9999); Usevar=mth7 mth8 mth9 mth10; classes=c(3); Analysis: Type = mixture missing; starts=70 7; Model: %overall% i s | mth7@0 mth8@1 mth9@2 mth10@3; Output: tech11; Plot: type=plot3; series= mth7-mth10(*); *** 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: 14 1 WARNING(S) FOUND IN THE INPUT INSTRUCTIONS lsay_3.inp LSAY math outcomes Growth mixture model with 3-classes SUMMARY OF ANALYSIS Number of groups 1 Number of observations 3102 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 MTH7 MTH8 MTH9 MTH10 Continuous latent variables I S Categorical latent variables C Estimator MLR Information matrix OBSERVED Optimization Specifications for the Quasi-Newton Algorithm for Continuous Outcomes Maximum number of iterations 1000 Convergence criterion 0.100D-05 Optimization Specifications for the EM Algorithm Maximum number of iterations 500 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 Random Starts Specifications Number of initial stage starts 70 Number of final stage starts 7 Number of initial stage iterations 10 Initial stage convergence criterion 0.100D+01 Random starts scale 0.500D+01 Random seed for generating random starts 0 Input data file(s) lsay.dat Input data format FREE SUMMARY OF DATA Number of patterns 15 Number of y patterns 15 Number of u patterns 0 COVARIANCE COVERAGE OF DATA Minimum covariance coverage value 0.100 PROPORTION OF DATA PRESENT FOR Y Covariance Coverage MTH7 MTH8 MTH9 MTH10 ________ ________ ________ ________ MTH7 0.988 MTH8 0.822 0.832 MTH9 0.714 0.657 0.722 MTH10 0.651 0.592 0.587 0.658 RANDOM STARTS RESULTS RANKED FROM THE BEST TO THE WORST LOGLIKELIHOOD VALUES Initial stage loglikelihood values, seeds, and initial stage start numbers: -33523.530 887676 22 -33525.247 749453 33 -33526.066 415931 10 -33526.079 366706 29 -33527.222 462953 7 -33532.200 127215 9 -33540.070 963053 43 -33545.716 443917 60 -33547.493 551639 55 -33548.195 851945 18 -33559.218 76974 16 -33559.577 533739 11 -33569.171 341041 34 -33571.197 405079 68 -33575.442 830392 35 -33577.935 352277 42 -33578.295 569131 26 -33581.895 685657 69 -33582.559 227563 63 -33584.287 481835 57 -33584.784 85462 51 -33585.607 903420 5 -33585.791 285380 1 -33586.836 120506 45 -33587.804 347515 24 -33589.776 93468 3 -33591.570 392418 28 -33594.712 565819 65 -33595.929 966014 37 -33598.308 432148 30 -33599.321 370466 41 -33600.284 399671 13 -33600.315 637345 19 -33602.382 573096 20 -33604.089 939021 8 -33604.410 645664 39 -33604.972 568859 49 -33606.666 650371 14 -33608.418 364676 27 -33608.584 987090 70 -33610.499 260601 36 -33610.569 608496 4 -33611.566 153942 31 -33612.103 311214 64 -33612.875 246261 38 -33616.255 68985 17 -33618.807 27071 15 -33619.010 789985 67 -33619.025 107446 12 -33619.127 804561 59 -33619.395 422103 62 -33621.257 915642 40 -33625.288 603842 61 -33625.472 372176 23 -33625.918 754100 56 -33626.587 902278 21 -33627.923 407168 44 -33632.888 207896 25 -33633.388 848163 47 -33635.365 253358 2 -33637.238 467339 66 -33637.762 761633 50 -33639.001 967237 48 -33639.143 626891 32 -33640.684 915107 54 -33640.729 195873 6 -33641.534 967902 52 -33641.558 unperturbed 0 -33642.127 136842 58 -33644.179 318230 46 -33644.614 259507 53 Loglikelihood values at local maxima, seeds, and initial stage start numbers: -33520.652 887676 22 -33520.652 749453 33 -33520.652 415931 10 -33520.652 127215 9 -33520.652 366706 29 -33521.950 963053 43 -33521.950 462953 7 THE MODEL ESTIMATION TERMINATED NORMALLY TESTS OF MODEL FIT Loglikelihood H0 Value -33520.652 H0 Scaling Correction Factor 1.800 for MLR Information Criteria Number of Free Parameters 15 Akaike (AIC) 67071.305 Bayesian (BIC) 67161.902 Sample-Size Adjusted BIC 67114.241 (n* = (n + 2) / 24) Entropy 0.502 FINAL CLASS COUNTS AND PROPORTIONS FOR THE LATENT CLASSES BASED ON THE ESTIMATED MODEL Latent Classes 1 1020.31757 0.32892 2 1320.04944 0.42555 3 761.63298 0.24553 FINAL CLASS COUNTS AND PROPORTIONS FOR THE LATENT CLASS PATTERNS BASED ON ESTIMATED POSTERIOR PROBABILITIES Latent Classes 1 1020.31749 0.32892 2 1320.04942 0.42555 3 761.63310 0.24553 CLASSIFICATION OF INDIVIDUALS BASED ON THEIR MOST LIKELY LATENT CLASS MEMBERSHIP Class Counts and Proportions Latent Classes 1 1124 0.36235 2 1398 0.45068 3 580 0.18698 Average Latent Class Probabilities for Most Likely Latent Class Membership (Row) by Latent Class (Column) 1 2 3 1 0.750 0.072 0.178 2 0.066 0.813 0.121 3 0.147 0.176 0.677 MODEL RESULTS Estimates S.E. Est./S.E. Latent Class 1 I | MTH7 1.000 0.000 0.000 MTH8 1.000 0.000 0.000 MTH9 1.000 0.000 0.000 MTH10 1.000 0.000 0.000 S | MTH7 0.000 0.000 0.000 MTH8 1.000 0.000 0.000 MTH9 2.000 0.000 0.000 MTH10 3.000 0.000 0.000 S WITH I 2.990 1.549 1.931 Means I 43.136 0.802 53.772 S 1.494 0.841 1.775 Intercepts MTH7 0.000 0.000 0.000 MTH8 0.000 0.000 0.000 MTH9 0.000 0.000 0.000 MTH10 0.000 0.000 0.000 Variances I 35.006 2.793 12.532 S 1.737 0.505 3.443 Residual Variances MTH7 16.229 1.368 11.864 MTH8 18.992 0.913 20.790 MTH9 16.557 0.975 16.981 MTH10 15.989 2.155 7.418 Latent Class 2 I | MTH7 1.000 0.000 0.000 MTH8 1.000 0.000 0.000 MTH9 1.000 0.000 0.000 MTH10 1.000 0.000 0.000 S | MTH7 0.000 0.000 0.000 MTH8 1.000 0.000 0.000 MTH9 2.000 0.000 0.000 MTH10 3.000 0.000 0.000 S WITH I 2.990 1.549 1.931 Means I 58.514 1.681 34.802 S 4.471 0.078 57.267 Intercepts MTH7 0.000 0.000 0.000 MTH8 0.000 0.000 0.000 MTH9 0.000 0.000 0.000 MTH10 0.000 0.000 0.000 Variances I 35.006 2.793 12.532 S 1.737 0.505 3.443 Residual Variances MTH7 16.229 1.368 11.864 MTH8 18.992 0.913 20.790 MTH9 16.557 0.975 16.981 MTH10 15.989 2.155 7.418 Latent Class 3 I | MTH7 1.000 0.000 0.000 MTH8 1.000 0.000 0.000 MTH9 1.000 0.000 0.000 MTH10 1.000 0.000 0.000 S | MTH7 0.000 0.000 0.000 MTH8 1.000 0.000 0.000 MTH9 2.000 0.000 0.000 MTH10 3.000 0.000 0.000 S WITH I 2.990 1.549 1.931 Means I 45.290 2.625 17.252 S 6.276 1.948 3.222 Intercepts MTH7 0.000 0.000 0.000 MTH8 0.000 0.000 0.000 MTH9 0.000 0.000 0.000 MTH10 0.000 0.000 0.000 Variances I 35.006 2.793 12.532 S 1.737 0.505 3.443 Residual Variances MTH7 16.229 1.368 11.864 MTH8 18.992 0.913 20.790 MTH9 16.557 0.975 16.981 MTH10 15.989 2.155 7.418 Categorical Latent Variables Means C#1 0.292 1.241 0.236 C#2 0.550 1.125 0.489 QUALITY OF NUMERICAL RESULTS Condition Number for the Information Matrix 0.133E-03 (ratio of smallest to largest eigenvalue) TECHNICAL 11 OUTPUT VUONG-LO-MENDELL-RUBIN LIKELIHOOD RATIO TEST FOR 2 (H0) VERSUS 3 CLASSES H0 Loglikelihood Value -33583.299 2 Times the Loglikelihood Difference 125.293 Difference in the Number of Parameters 3 Mean 136.486 Standard Deviation 192.297 P-Value 0.3382 LO-MENDELL-RUBIN ADJUSTED LRT TEST Value 120.305 P-Value 0.3480 PLOT INFORMATION The following plots are available: Histograms (sample values, estimated factor scores, estimated values) Scatterplots (sample values, estimated factor scores, estimated values) Sample means Estimated means Sample and estimated means Observed individual values Estimated individual values Estimated means and observed individual values Estimated means and estimated individual values Mixture distributions Beginning Time: 14:03:05 Ending Time: 14:04:02 Elapsed Time: 00:00:57 MUTHEN & MUTHEN 3463 Stoner Ave. Los Angeles, CA 90066 Tel: (310) 391-9971 Fax: (310) 391-8971 Web: www.StatModel.com Support: Support@StatModel.com Copyright (c) 1998-2005 Muthen & Muthen