Mplus VERSION 6.1 MUTHEN & MUTHEN 10/18/2010 11:56 AM INPUT INSTRUCTIONS Title: Data: File = StarD Ratings 1-23-09.dat; Variable: Names = ID DATE_0 CSOIN_0 CMNIN_0 CEMIN_0 CHYSM_0 CMDSD_0 CAPDC_0 CAPIN_0 CWTDC_0 CWTIN_0 CCNTR_0 CVWSF_0 CSUIC_0 CINTR_0 CENGY_0 CSLOW_0 CAGIT_0 y0 DATE_2 CSOIN_2 CMNIN_2 CEMIN_2 CHYSM_2 CMDSD_2 CAPDC_2 CAPIN_2 CWTDC_2 CWTIN_2 CCNTR_2 CVWSF_2 CSUIC_2 CINTR_2 CENGY_2 CSLOW_2 CAGIT_2 y1 DATE_4 CSOIN_4 CMNIN_4 CEMIN_4 CHYSM_4 CMDSD_4 CAPDC_4 CAPIN_4 CWTDC_4 CWTIN_4 CCNTR_4 CVWSF_4 CSUIC_4 CINTR_4 CENGY_4 CSLOW_4 CAGIT_4 y2 DATE_6 SOIN_6 CMNIN_6 CEMIN_6 CHYSM_6 CMDSD_6 CAPDC_6 CAPIN_6 CWTDC_6 CWTIN_6 CCNTR_6 CVWSF_6 CSUIC_6 CINTR_6 CENGY_6 CSLOW_6 CAGIT_6 y3 DATE_9 CSOIN_9 CMNIN_9 CEMIN_9 CHYSM_9 CMDSD_9 CAPDC_9 CAPIN_9 CWTDC_9 CWTIN_9 CCNTR_9 CVWSF_9 CSUIC_9 CINTR_9 CENGY_9 CSLOW_9 CAGIT_9 y4 DATE_12 CSOIN_12 CMNIN_12 CEMIN_12 CHYSM_12 CMDSD_12 CAPDC_12 CAPIN_12 CWTDC_12 CWTIN_12 CCNTR_12 CVWSF_12 CSUIC_12 CINTR_12 CENGY_12 CSLOW_12 CAGIT_12 y5 DATE_14 CSOIN_14 CMNIN_14 CEMIN_14 CHYSM_14 CMDSD_14 CAPDC_14 CAPIN_14 CWTDC_14 CWTIN_14 CCNTR_14 CVWSF_14 CSUIC_14 CINTR_14 CENGY_14 CSLOW_14 CAGIT_14 y6 !Self-Ratings. SSOIN_0 SMNIN_0 SEMIN_0 SHYSM_0 SMDSD_0 SAPDC_0 SAPIN_0 SWTDC_0 SWTIN_0 SCNTR_0 SVWSF_0 SSUIC_0 SINTR_0 SENGY_0 SSLOW_0 SAGIT_0 QSTOT_0 SSOIN_2 SMNIN_2 SEMIN_2 SHYSM_2 SMDSD_2 SAPDC_2 SAPIN_2 SWTDC_2 SWTIN_2 SCNTR_2 SVWSF_2 SSUIC_2 SINTR_2 SENGY_2 SSLOW_2 SAGIT_2 QSTOT_2 SSOIN_4 SMNIN_4 SEMIN_4 SHYSM_4 SMDSD_4 SAPDC_4 SAPIN_4 SWTDC_4 SWTIN_4 SCNTR_4 SVWSF_4 SSUIC_4 SINTR_4 SENGY_4 SSLOW_4 SAGIT_4 QSTOT_4 SSOIN_6 SMNIN_6 SEMIN_6 SHYSM_6 SMDSD_6 SAPDC_6 SAPIN_6 SWTDC_6 SWTIN_6 SCNTR_6 SVWSF_6 SSUIC_6 SINTR_6 SENGY_6 SSLOW_6 SAGIT_6 QSTOT_6 SSOIN_9 SMNIN_9 SEMIN_9 SHYSM_9 SMDSD_9 SAPDC_9 SAPIN_9 SWTDC_9 SWTIN_9 SCNTR_9 SVWSF_9 SSUIC_9 SINTR_9 SENGY_9 SSLOW_9 SAGIT_9 QSTOT_9 SSOIN_12 SMNIN_12 SEMIN_12 SHYSM_12 SMDSD_12 SAPDC_12 SAPIN_12 SWTDC_12 SWTIN_12 SCNTR_12 SVWSF_12 SSUIC_12 SINTR_12 SENGY_12 SSLOW_12 SAGIT_12 QSTOT_12 SSOIN_14 SMNIN_14 SEMIN_14 SHYSM_14 SMDSD_14 SAPDC_14 SAPIN_14 SWTDC_14 SWTIN_14 SCNTR_14 SVWSF_14 SSUIC_14 SINTR_14 SENGY_14 SSLOW_14 SAGIT_14 QSTOT_14; Missing = all (-9999); usev = y0 y1 y2 y3 y4 y5; classes = c(4); Analysis: type = mixture; starts = 2000 400; process = 4(starts); interactive = control.dat; Model: %overall% i s q | y0@0 y1@.2 y2@.4 y3@.6 y4@.9 y5@1.2; Plot: type = plot3; series = y0-y5(s); Output: tech1 sampstat residual; INPUT READING TERMINATED NORMALLY SUMMARY OF ANALYSIS Number of groups 1 Number of observations 4041 Number of dependent variables 6 Number of independent variables 0 Number of continuous latent variables 3 Number of categorical latent variables 1 Observed dependent variables Continuous Y0 Y1 Y2 Y3 Y4 Y5 Continuous latent variables I S Q 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 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 random starts 2000 Number of final stage optimizations 400 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) StarD Ratings 1-23-09.dat Input data format FREE SUMMARY OF DATA Number of missing data patterns 34 Number of y missing data patterns 34 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 Y0 Y1 Y2 Y3 Y4 ________ ________ ________ ________ ________ Y0 1.000 Y1 0.790 0.790 Y2 0.693 0.607 0.694 Y3 0.677 0.603 0.564 0.677 Y4 0.571 0.508 0.490 0.511 0.571 Y5 0.385 0.349 0.333 0.343 0.328 Covariance Coverage Y5 ________ Y5 0.385 SAMPLE STATISTICS ESTIMATED SAMPLE STATISTICS Means Y0 Y1 Y2 Y3 Y4 ________ ________ ________ ________ ________ 1 16.405 12.241 10.731 9.478 8.610 Means Y5 ________ 1 7.709 Covariances Y0 Y1 Y2 Y3 Y4 ________ ________ ________ ________ ________ Y0 11.660 Y1 8.123 20.452 Y2 7.373 15.157 24.102 Y3 6.406 13.488 17.718 25.210 Y4 5.943 12.762 16.378 18.888 26.947 Y5 5.648 12.615 15.576 17.805 21.022 Covariances Y5 ________ Y5 28.531 Correlations Y0 Y1 Y2 Y3 Y4 ________ ________ ________ ________ ________ Y0 1.000 Y1 0.526 1.000 Y2 0.440 0.683 1.000 Y3 0.374 0.594 0.719 1.000 Y4 0.335 0.544 0.643 0.725 1.000 Y5 0.310 0.522 0.594 0.664 0.758 Correlations Y5 ________ Y5 1.000 MAXIMUM LOG-LIKELIHOOD VALUE FOR THE UNRESTRICTED (H1) MODEL IS -44757.802 RANDOM STARTS RESULTS RANKED FROM THE BEST TO THE WORST LOGLIKELIHOOD VALUES Final stage loglikelihood values at local maxima, seeds, and initial stage start numbers: -44890.482 155749 960 -44890.482 242621 1642 -44890.482 483538 1897 -44890.482 313306 612 -44890.482 574942 558 -44890.482 853195 431 -44890.482 995875 547 -44890.482 39810 966 -44890.482 188498 258 -44890.482 677062 680 -44890.482 311484 1135 -44890.482 414110 1821 -44890.482 137863 1030 -44890.482 102845 1640 -44890.482 253358 2 -44890.482 492814 1746 -44890.482 289739 1348 -44890.482 432148 30 -44890.482 509733 130 -44890.482 229744 1096 -44890.482 605565 404 -44890.482 696878 1643 -44890.482 908674 1748 -44890.482 329944 1506 -44890.482 703313 1415 -44890.482 721392 768 -44890.482 471512 1951 -44890.482 542577 1004 -44890.482 514629 1695 -44890.482 497522 502 -44890.482 700349 401 -44890.482 628997 1188 -44890.482 576220 115 -44890.482 161421 519 -44890.482 460521 1032 -44890.482 782200 84 -44890.482 657746 1455 -44890.482 695155 150 -44890.482 860772 174 -44890.482 933578 506 -44890.482 792993 859 -44890.482 871438 561 -44890.482 865693 1380 -44890.482 614032 1226 -44890.482 961454 665 -44890.482 82200 830 -44890.482 751153 110 -44890.482 314034 513 -44890.482 503540 1984 -44890.482 526077 1903 -44890.482 295033 1052 -44890.482 843555 952 -44890.482 533691 1974 -44890.482 703098 1700 -44890.482 791285 416 -44890.482 476338 1418 -44890.482 280104 1875 -44890.482 859432 770 -44890.482 298275 418 -44890.482 502157 799 -44890.482 688839 273 -44890.482 791678 974 -44890.482 707032 1229 -44890.482 276102 599 -44903.449 861413 1154 -44903.449 294948 1999 -44903.449 871188 1261 -44903.449 622290 880 -44903.449 216565 474 -44903.449 573096 20 -44903.449 533355 1328 -44903.449 456213 160 -44903.449 829540 324 -44903.449 914193 1411 -44903.449 168762 200 -44903.449 269800 1686 -44903.449 214681 824 -44903.449 775996 1481 -44903.449 141203 1639 -44903.449 237332 661 -44903.449 438144 271 -44903.449 584397 428 -44903.449 200041 810 -44903.449 321112 1103 -44903.449 299108 1420 -44903.449 516908 1977 -44903.449 344109 1044 -44903.449 404840 1210 -44903.449 760850 739 -44903.449 848163 47 -44903.449 944186 541 -44903.449 152496 123 -44903.449 207896 25 -44903.449 392766 331 -44903.449 36714 201 -44903.449 925741 1567 -44903.449 917570 1168 -44903.449 240994 1598 -44903.449 93468 3 -44903.449 373505 88 -44903.449 910119 1886 -44903.449 504861 1771 -44903.449 608208 1931 -44903.449 117891 1818 -44903.449 830570 369 -44903.449 270428 1873 -44903.449 788293 1003 -44903.449 72450 1922 -44903.449 199867 1584 -44903.449 707200 1005 -44903.449 269105 1809 -44903.449 404042 675 -44903.449 640833 434 -44903.449 453384 1545 -44903.449 207299 1039 -44903.449 157736 1524 -44903.449 471398 74 -44903.449 638977 643 -44903.449 256197 1923 -44903.449 496703 758 -44903.449 67009 564 -44903.449 476393 705 -44903.449 696266 1192 -44903.449 665858 1238 -44903.449 356754 1372 -44903.449 694918 1206 -44903.449 195353 225 -44903.449 583281 1907 -44903.449 654377 1684 -44903.449 618760 489 -44903.449 87856 1116 -44903.449 444166 1443 -44903.449 715255 523 -44903.449 217415 1240 -44903.449 951709 348 -44903.449 91231 727 -44903.449 160041 1384 -44903.449 638118 1588 -44903.449 7886 804 -44903.449 60038 1064 -44903.449 637095 207 -44903.449 348892 1010 -44903.449 705445 1117 -44903.449 965994 396 -44903.449 473343 844 -44903.449 81954 1823 -44903.449 556955 1141 -44903.449 76974 16 -44903.449 667170 1379 -44903.449 752292 1750 -44903.449 366533 484 -44903.449 499150 216 -44903.449 995249 525 -44903.449 128569 1839 -44903.449 357866 968 -44903.449 401377 1889 -44903.449 326355 1576 -44903.449 923767 1979 -44903.449 949086 1426 -44903.449 682178 1344 -44903.449 902278 21 -44903.449 945797 1471 -44903.449 346838 1170 -44903.449 16207 1568 -44903.449 358488 264 -44903.449 604441 1026 -44903.449 973369 202 -44903.449 954914 911 -44903.449 738142 1970 -44903.449 343926 624 -44903.449 64717 1631 -44903.449 153394 429 -44903.449 726744 939 -44903.449 778331 1280 -44903.449 370481 742 -44903.449 788599 1180 -44903.449 962482 1742 -44903.449 494663 1498 -44903.449 136842 58 -44903.449 793035 187 -44903.449 195763 358 -44903.449 501592 1738 -44903.449 899876 1808 -44903.449 496344 808 -44903.449 234114 1312 -44903.449 118438 601 -44903.449 860643 1844 -44903.449 289415 1086 -44903.449 100595 1766 -44903.449 371246 101 -44903.449 321390 133 -44903.449 282955 1073 -44903.449 422004 1107 -44903.449 606769 1870 -44903.449 701526 1057 -44903.449 241197 747 -44903.449 889774 954 -44903.449 814975 129 -44903.449 381543 1407 -44903.449 264081 186 -44903.449 738069 1830 -44903.449 119225 1658 -44903.449 257136 1774 -44903.449 50887 389 -44903.449 666720 671 -44903.449 319575 499 -44903.449 809240 543 -44903.449 364895 1590 -44903.449 975027 1552 -44903.449 401601 1267 -44903.449 474357 789 -44903.449 22089 143 -44903.449 95219 1871 -44903.449 76736 1142 -44903.449 704051 1083 -44903.449 945083 1842 -44903.449 403287 1558 -44903.449 219602 1247 -44903.449 723096 1388 -44903.449 749635 420 -44903.449 438792 1525 -44903.449 669634 335 -44903.449 978153 1772 -44903.449 608496 4 -44903.449 774692 1764 -44903.449 166851 638 -44903.449 127362 757 -44903.449 140442 500 -44903.449 11984 934 -44903.449 566739 575 -44903.449 484406 421 -44903.449 115028 1718 -44903.449 887580 493 -44903.449 232226 235 -44903.449 863849 1122 -44903.449 483369 270 -44903.449 898058 1586 -44903.449 618000 190 -44903.449 798541 1266 -44903.449 485256 371 -44903.449 739486 1699 -44903.449 312754 562 -44903.449 622873 1218 -44903.449 804104 566 -44903.449 408713 450 -44903.449 96941 890 -44903.449 289928 1529 -44903.449 227786 1964 -44903.449 481835 57 -44903.449 393453 1355 -44903.449 432490 1462 -44903.449 197621 1172 -44903.449 712531 631 -44903.449 495490 990 -44903.449 963967 941 -44903.449 783102 433 -44903.449 153053 378 -44903.449 964999 1522 -44903.449 383986 159 -44903.449 129198 1928 -44903.449 856612 700 -44903.449 299700 932 -44903.449 847088 750 -44903.449 392751 480 -44903.449 351622 551 -44903.449 680987 1926 -44903.449 530559 1845 -44903.449 512403 719 -44903.449 822698 621 -44903.449 799404 1490 -44903.449 673767 1736 -44903.449 165375 1514 -44903.449 374630 1869 -44903.449 244349 736 -44903.449 739214 807 -44903.449 382611 1990 -44903.449 275475 413 -44903.449 158701 1058 -44903.449 685155 1761 -44903.449 491386 1787 -44907.412 614009 317 -44907.412 779882 1012 -44907.412 266425 2000 -44907.412 987012 1067 -44907.412 268896 124 -44907.412 537905 1335 -44907.412 414059 1200 -44907.412 115218 1382 -44907.412 166220 1502 -44907.412 259507 53 -44907.412 926762 704 -44907.412 150531 154 -44907.412 89970 223 -44907.412 940258 1242 -44907.412 913926 1381 -44907.412 451258 848 -44907.412 697866 1018 -44907.412 313278 1887 -44907.412 160326 546 -44907.412 999211 628 -44907.412 847341 1398 -44907.412 659773 663 -44907.412 406263 1618 -44907.412 425149 878 -44907.412 638032 1706 -44907.412 415502 194 -44907.412 921023 782 -44907.412 508445 946 -44907.412 314757 345 -44907.412 772131 407 -44907.412 349562 359 -44907.412 650354 577 -44907.412 525403 1184 -44907.412 733175 1857 -44907.412 180878 1246 -44907.412 331681 549 -44907.412 46502 714 -44907.412 469158 1145 -44907.412 262022 1467 -44907.412 926797 406 -44907.412 154575 539 -44907.412 626208 698 -44907.412 967902 52 -44907.412 195873 6 -44907.412 442672 1799 -44907.412 689529 516 -44907.412 816796 1728 -44907.412 105656 909 -44907.412 706042 1129 -44907.412 568405 233 -44907.412 490123 995 -44907.412 654136 167 -44907.412 98720 1059 -44907.412 91710 1908 -44907.412 810705 626 -44907.412 177936 726 -44907.412 853283 1854 -44907.412 74518 927 -44907.412 802256 477 -44907.412 372158 1404 -44907.412 710833 1179 -44907.412 997222 229 -44907.412 812578 1865 -44907.412 396795 323 -44907.412 319144 176 -44907.412 686384 690 -44907.412 405079 68 -44907.412 36183 1349 -44907.412 967588 1495 -44907.412 140987 1093 -44907.412 486646 586 -44907.412 452606 1675 -44907.412 654884 1352 -44907.412 79212 517 -44908.759 410023 1864 -44908.759 918558 412 -44908.759 358074 560 -44916.072 200463 1826 -44916.072 127359 1737 -44916.072 419870 1442 -44916.072 282261 1274 -44916.072 818353 1256 -44916.072 580405 286 -44916.072 939870 655 -44916.072 353983 1235 -44916.072 802682 419 -44916.072 449258 1356 -44916.072 208620 965 -44916.072 929361 1971 -44916.072 32468 1606 -44916.072 265218 924 -44916.072 937727 1011 -44916.072 225932 1285 -44916.072 710154 831 -44916.072 696830 668 -44916.072 555330 1400 -44916.072 919217 1501 -44921.080 576596 99 -44921.080 149777 1434 -44921.644 638611 524 -44921.709 642386 662 -44921.709 848795 1757 -44926.445 263877 1659 -44926.849 907810 795 -44926.849 426557 1306 -44926.849 224151 973 -44936.944 220454 288 -44936.944 939021 8 -44936.944 428893 1859 -44936.944 898745 466 -44936.944 303959 1063 -44936.944 845197 1456 -44936.944 903420 5 -44936.944 298553 773 -44936.944 159259 1665 -44936.944 170118 238 -44938.700 626277 1510 -44938.700 172133 1697 -44942.021 354208 196 -44942.021 288738 940 WARNING: WHEN ESTIMATING A MODEL WITH MORE THAN TWO CLASSES, IT MAY BE NECESSARY TO INCREASE THE NUMBER OF RANDOM STARTS USING THE STARTS OPTION TO AVOID LOCAL MAXIMA. THE MODEL ESTIMATION TERMINATED NORMALLY MODEL FIT INFORMATION Number of Free Parameters 27 Loglikelihood H0 Value -44890.482 H0 Scaling Correction Factor 1.314 for MLR Information Criteria Akaike (AIC) 89834.964 Bayesian (BIC) 90005.179 Sample-Size Adjusted BIC 89919.385 (n* = (n + 2) / 24) FINAL CLASS COUNTS AND PROPORTIONS FOR THE LATENT CLASSES BASED ON THE ESTIMATED MODEL Latent Classes 1 2200.86010 0.54463 2 1086.87955 0.26896 3 129.29100 0.03199 4 623.96934 0.15441 FINAL CLASS COUNTS AND PROPORTIONS FOR THE LATENT CLASS PATTERNS BASED ON ESTIMATED POSTERIOR PROBABILITIES Latent Classes 1 2200.86011 0.54463 2 1086.87954 0.26896 3 129.29100 0.03199 4 623.96935 0.15441 CLASSIFICATION QUALITY Entropy 0.576 CLASSIFICATION OF INDIVIDUALS BASED ON THEIR MOST LIKELY LATENT CLASS MEMBERSHIP Class Counts and Proportions Latent Classes 1 2528 0.62559 2 849 0.21010 3 52 0.01287 4 612 0.15145 Average Latent Class Probabilities for Most Likely Latent Class Membership (Row) by Latent Class (Column) 1 2 3 4 1 0.790 0.154 0.026 0.030 2 0.188 0.660 0.017 0.135 3 0.102 0.115 0.703 0.080 4 0.062 0.215 0.022 0.701 MODEL RESULTS Two-Tailed Estimate S.E. Est./S.E. P-Value Latent Class 1 I | Y0 1.000 0.000 999.000 999.000 Y1 1.000 0.000 999.000 999.000 Y2 1.000 0.000 999.000 999.000 Y3 1.000 0.000 999.000 999.000 Y4 1.000 0.000 999.000 999.000 Y5 1.000 0.000 999.000 999.000 S | Y0 0.000 0.000 999.000 999.000 Y1 0.200 0.000 999.000 999.000 Y2 0.400 0.000 999.000 999.000 Y3 0.600 0.000 999.000 999.000 Y4 0.900 0.000 999.000 999.000 Y5 1.200 0.000 999.000 999.000 Q | Y0 0.000 0.000 999.000 999.000 Y1 0.040 0.000 999.000 999.000 Y2 0.160 0.000 999.000 999.000 Y3 0.360 0.000 999.000 999.000 Y4 0.810 0.000 999.000 999.000 Y5 1.440 0.000 999.000 999.000 S WITH I -3.830 1.451 -2.640 0.008 Q WITH I -0.865 1.024 -0.845 0.398 S -39.462 6.994 -5.642 0.000 Means I 15.198 0.104 145.839 0.000 S -19.480 0.777 -25.058 0.000 Q 8.652 0.584 14.804 0.000 Intercepts Y0 0.000 0.000 999.000 999.000 Y1 0.000 0.000 999.000 999.000 Y2 0.000 0.000 999.000 999.000 Y3 0.000 0.000 999.000 999.000 Y4 0.000 0.000 999.000 999.000 Y5 0.000 0.000 999.000 999.000 Variances I 5.906 0.407 14.510 0.000 S 56.902 10.620 5.358 0.000 Q 30.248 4.616 6.553 0.000 Residual Variances Y0 4.346 0.383 11.336 0.000 Y1 9.549 0.305 31.354 0.000 Y2 7.303 0.330 22.156 0.000 Y3 6.269 0.322 19.450 0.000 Y4 7.295 0.451 16.160 0.000 Y5 3.372 0.701 4.808 0.000 Latent Class 2 I | Y0 1.000 0.000 999.000 999.000 Y1 1.000 0.000 999.000 999.000 Y2 1.000 0.000 999.000 999.000 Y3 1.000 0.000 999.000 999.000 Y4 1.000 0.000 999.000 999.000 Y5 1.000 0.000 999.000 999.000 S | Y0 0.000 0.000 999.000 999.000 Y1 0.200 0.000 999.000 999.000 Y2 0.400 0.000 999.000 999.000 Y3 0.600 0.000 999.000 999.000 Y4 0.900 0.000 999.000 999.000 Y5 1.200 0.000 999.000 999.000 Q | Y0 0.000 0.000 999.000 999.000 Y1 0.040 0.000 999.000 999.000 Y2 0.160 0.000 999.000 999.000 Y3 0.360 0.000 999.000 999.000 Y4 0.810 0.000 999.000 999.000 Y5 1.440 0.000 999.000 999.000 S WITH I -3.830 1.451 -2.640 0.008 Q WITH I -0.865 1.024 -0.845 0.398 S -39.462 6.994 -5.642 0.000 Means I 16.349 0.283 57.850 0.000 S -12.569 1.633 -7.698 0.000 Q 6.409 1.409 4.548 0.000 Intercepts Y0 0.000 0.000 999.000 999.000 Y1 0.000 0.000 999.000 999.000 Y2 0.000 0.000 999.000 999.000 Y3 0.000 0.000 999.000 999.000 Y4 0.000 0.000 999.000 999.000 Y5 0.000 0.000 999.000 999.000 Variances I 5.906 0.407 14.510 0.000 S 56.902 10.620 5.358 0.000 Q 30.248 4.616 6.553 0.000 Residual Variances Y0 4.346 0.383 11.336 0.000 Y1 9.549 0.305 31.354 0.000 Y2 7.303 0.330 22.156 0.000 Y3 6.269 0.322 19.450 0.000 Y4 7.295 0.451 16.160 0.000 Y5 3.372 0.701 4.808 0.000 Latent Class 3 I | Y0 1.000 0.000 999.000 999.000 Y1 1.000 0.000 999.000 999.000 Y2 1.000 0.000 999.000 999.000 Y3 1.000 0.000 999.000 999.000 Y4 1.000 0.000 999.000 999.000 Y5 1.000 0.000 999.000 999.000 S | Y0 0.000 0.000 999.000 999.000 Y1 0.200 0.000 999.000 999.000 Y2 0.400 0.000 999.000 999.000 Y3 0.600 0.000 999.000 999.000 Y4 0.900 0.000 999.000 999.000 Y5 1.200 0.000 999.000 999.000 Q | Y0 0.000 0.000 999.000 999.000 Y1 0.040 0.000 999.000 999.000 Y2 0.160 0.000 999.000 999.000 Y3 0.360 0.000 999.000 999.000 Y4 0.810 0.000 999.000 999.000 Y5 1.440 0.000 999.000 999.000 S WITH I -3.830 1.451 -2.640 0.008 Q WITH I -0.865 1.024 -0.845 0.398 S -39.462 6.994 -5.642 0.000 Means I 18.301 0.541 33.859 0.000 S -39.500 2.811 -14.054 0.000 Q 30.815 3.401 9.061 0.000 Intercepts Y0 0.000 0.000 999.000 999.000 Y1 0.000 0.000 999.000 999.000 Y2 0.000 0.000 999.000 999.000 Y3 0.000 0.000 999.000 999.000 Y4 0.000 0.000 999.000 999.000 Y5 0.000 0.000 999.000 999.000 Variances I 5.906 0.407 14.510 0.000 S 56.902 10.620 5.358 0.000 Q 30.248 4.616 6.553 0.000 Residual Variances Y0 4.346 0.383 11.336 0.000 Y1 9.549 0.305 31.354 0.000 Y2 7.303 0.330 22.156 0.000 Y3 6.269 0.322 19.450 0.000 Y4 7.295 0.451 16.160 0.000 Y5 3.372 0.701 4.808 0.000 Latent Class 4 I | Y0 1.000 0.000 999.000 999.000 Y1 1.000 0.000 999.000 999.000 Y2 1.000 0.000 999.000 999.000 Y3 1.000 0.000 999.000 999.000 Y4 1.000 0.000 999.000 999.000 Y5 1.000 0.000 999.000 999.000 S | Y0 0.000 0.000 999.000 999.000 Y1 0.200 0.000 999.000 999.000 Y2 0.400 0.000 999.000 999.000 Y3 0.600 0.000 999.000 999.000 Y4 0.900 0.000 999.000 999.000 Y5 1.200 0.000 999.000 999.000 Q | Y0 0.000 0.000 999.000 999.000 Y1 0.040 0.000 999.000 999.000 Y2 0.160 0.000 999.000 999.000 Y3 0.360 0.000 999.000 999.000 Y4 0.810 0.000 999.000 999.000 Y5 1.440 0.000 999.000 999.000 S WITH I -3.830 1.451 -2.640 0.008 Q WITH I -0.865 1.024 -0.845 0.398 S -39.462 6.994 -5.642 0.000 Means I 18.911 0.214 88.232 0.000 S -8.816 0.702 -12.554 0.000 Q 6.180 0.701 8.813 0.000 Intercepts Y0 0.000 0.000 999.000 999.000 Y1 0.000 0.000 999.000 999.000 Y2 0.000 0.000 999.000 999.000 Y3 0.000 0.000 999.000 999.000 Y4 0.000 0.000 999.000 999.000 Y5 0.000 0.000 999.000 999.000 Variances I 5.906 0.407 14.510 0.000 S 56.902 10.620 5.358 0.000 Q 30.248 4.616 6.553 0.000 Residual Variances Y0 4.346 0.383 11.336 0.000 Y1 9.549 0.305 31.354 0.000 Y2 7.303 0.330 22.156 0.000 Y3 6.269 0.322 19.450 0.000 Y4 7.295 0.451 16.160 0.000 Y5 3.372 0.701 4.808 0.000 Categorical Latent Variables Means C#1 1.261 0.138 9.161 0.000 C#2 0.555 0.131 4.235 0.000 C#3 -1.574 0.356 -4.421 0.000 QUALITY OF NUMERICAL RESULTS Condition Number for the Information Matrix 0.835E-05 (ratio of smallest to largest eigenvalue) RESIDUAL OUTPUT ESTIMATED MODEL AND RESIDUALS (OBSERVED - ESTIMATED) FOR CLASS 1 Model Estimated Means Y0 Y1 Y2 Y3 Y4 ________ ________ ________ ________ ________ 1 15.198 11.649 8.791 6.625 4.675 Model Estimated Means Y5 ________ 1 4.281 Residuals for Means Y0 Y1 Y2 Y3 Y4 ________ ________ ________ ________ ________ 1 0.283 -1.158 -0.299 0.262 0.744 Residuals for Means Y5 ________ 1 -0.281 Model Estimated Covariances Y0 Y1 Y2 Y3 Y4 ________ ________ ________ ________ ________ Y0 10.252 Y1 5.105 15.547 Y2 4.236 6.287 14.695 Y3 3.297 5.971 7.554 14.313 Y4 1.758 4.367 6.025 6.732 13.307 Y5 0.064 1.403 2.369 2.963 3.153 Model Estimated Covariances Y5 ________ Y5 5.877 Residuals for Covariances Y0 Y1 Y2 Y3 Y4 ________ ________ ________ ________ ________ Y0 -0.034 Y1 0.485 0.238 Y2 -0.428 1.162 0.902 Y3 -0.787 -0.967 -1.249 -0.918 Y4 -0.653 -1.090 -1.533 -1.906 -2.605 Y5 0.593 0.628 0.593 0.318 0.998 Residuals for Covariances Y5 ________ Y5 -0.340 ESTIMATED MODEL AND RESIDUALS (OBSERVED - ESTIMATED) FOR CLASS 2 Model Estimated Means Y0 Y1 Y2 Y3 Y4 ________ ________ ________ ________ ________ 1 16.349 14.091 12.346 11.114 10.228 Model Estimated Means Y5 ________ 1 10.495 Residuals for Means Y0 Y1 Y2 Y3 Y4 ________ ________ ________ ________ ________ 1 0.175 -0.835 -0.163 0.097 0.286 Residuals for Means Y5 ________ 1 -0.385 Model Estimated Covariances Y0 Y1 Y2 Y3 Y4 ________ ________ ________ ________ ________ Y0 10.252 Y1 5.105 15.547 Y2 4.236 6.287 14.695 Y3 3.297 5.971 7.554 14.313 Y4 1.758 4.367 6.025 6.732 13.307 Y5 0.064 1.403 2.369 2.963 3.153 Model Estimated Covariances Y5 ________ Y5 5.877 Residuals for Covariances Y0 Y1 Y2 Y3 Y4 ________ ________ ________ ________ ________ Y0 0.021 Y1 -0.234 -1.169 Y2 -0.130 -1.534 0.312 Y3 -1.562 -3.222 -3.087 0.107 Y4 -0.774 -4.120 -3.618 -5.445 -0.591 Y5 1.752 -1.017 -3.488 -6.265 -4.627 Residuals for Covariances Y5 ________ Y5 0.258 ESTIMATED MODEL AND RESIDUALS (OBSERVED - ESTIMATED) FOR CLASS 3 Model Estimated Means Y0 Y1 Y2 Y3 Y4 ________ ________ ________ ________ ________ 1 18.301 11.634 7.432 5.695 7.712 Model Estimated Means Y5 ________ 1 15.276 Residuals for Means Y0 Y1 Y2 Y3 Y4 ________ ________ ________ ________ ________ 1 0.147 -0.767 0.009 0.449 0.092 Residuals for Means Y5 ________ 1 0.094 Model Estimated Covariances Y0 Y1 Y2 Y3 Y4 ________ ________ ________ ________ ________ Y0 10.252 Y1 5.105 15.547 Y2 4.236 6.287 14.695 Y3 3.297 5.971 7.554 14.313 Y4 1.758 4.367 6.025 6.732 13.307 Y5 0.064 1.403 2.369 2.963 3.153 Model Estimated Covariances Y5 ________ Y5 5.877 Residuals for Covariances Y0 Y1 Y2 Y3 Y4 ________ ________ ________ ________ ________ Y0 -1.114 Y1 -0.279 5.634 Y2 -1.924 2.984 -0.708 Y3 -0.197 1.853 -1.310 -2.617 Y4 -1.595 2.165 2.608 -1.563 3.223 Y5 2.448 26.760 16.150 11.980 -0.283 Residuals for Covariances Y5 ________ Y5 3.475 ESTIMATED MODEL AND RESIDUALS (OBSERVED - ESTIMATED) FOR CLASS 4 Model Estimated Means Y0 Y1 Y2 Y3 Y4 ________ ________ ________ ________ ________ 1 18.911 17.395 16.373 15.846 15.983 Model Estimated Means Y5 ________ 1 17.232 Residuals for Means Y0 Y1 Y2 Y3 Y4 ________ ________ ________ ________ ________ 1 0.121 -0.742 -0.013 -0.060 -0.034 Residuals for Means Y5 ________ 1 -0.511 Model Estimated Covariances Y0 Y1 Y2 Y3 Y4 ________ ________ ________ ________ ________ Y0 10.252 Y1 5.105 15.547 Y2 4.236 6.287 14.695 Y3 3.297 5.971 7.554 14.313 Y4 1.758 4.367 6.025 6.732 13.307 Y5 0.064 1.403 2.369 2.963 3.153 Model Estimated Covariances Y5 ________ Y5 5.877 Residuals for Covariances Y0 Y1 Y2 Y3 Y4 ________ ________ ________ ________ ________ Y0 -1.360 Y1 -1.250 -1.742 Y2 -0.248 -1.563 0.626 Y3 -2.912 -2.764 -5.807 0.434 Y4 -2.201 -1.679 -7.750 -10.591 -0.040 Y5 -5.642 -11.940 -14.362 -20.953 -25.508 Residuals for Covariances Y5 ________ Y5 0.838 TECHNICAL 1 OUTPUT PARAMETER SPECIFICATION FOR LATENT CLASS 1 NU Y0 Y1 Y2 Y3 Y4 ________ ________ ________ ________ ________ 1 0 0 0 0 0 NU Y5 ________ 1 0 LAMBDA I S Q ________ ________ ________ Y0 0 0 0 Y1 0 0 0 Y2 0 0 0 Y3 0 0 0 Y4 0 0 0 Y5 0 0 0 THETA Y0 Y1 Y2 Y3 Y4 ________ ________ ________ ________ ________ Y0 1 Y1 0 2 Y2 0 0 3 Y3 0 0 0 4 Y4 0 0 0 0 5 Y5 0 0 0 0 0 THETA Y5 ________ Y5 6 ALPHA I S Q ________ ________ ________ 1 7 8 9 BETA I S Q ________ ________ ________ I 0 0 0 S 0 0 0 Q 0 0 0 PSI I S Q ________ ________ ________ I 10 S 11 12 Q 13 14 15 PARAMETER SPECIFICATION FOR LATENT CLASS 2 NU Y0 Y1 Y2 Y3 Y4 ________ ________ ________ ________ ________ 1 0 0 0 0 0 NU Y5 ________ 1 0 LAMBDA I S Q ________ ________ ________ Y0 0 0 0 Y1 0 0 0 Y2 0 0 0 Y3 0 0 0 Y4 0 0 0 Y5 0 0 0 THETA Y0 Y1 Y2 Y3 Y4 ________ ________ ________ ________ ________ Y0 1 Y1 0 2 Y2 0 0 3 Y3 0 0 0 4 Y4 0 0 0 0 5 Y5 0 0 0 0 0 THETA Y5 ________ Y5 6 ALPHA I S Q ________ ________ ________ 1 16 17 18 BETA I S Q ________ ________ ________ I 0 0 0 S 0 0 0 Q 0 0 0 PSI I S Q ________ ________ ________ I 10 S 11 12 Q 13 14 15 PARAMETER SPECIFICATION FOR LATENT CLASS 3 NU Y0 Y1 Y2 Y3 Y4 ________ ________ ________ ________ ________ 1 0 0 0 0 0 NU Y5 ________ 1 0 LAMBDA I S Q ________ ________ ________ Y0 0 0 0 Y1 0 0 0 Y2 0 0 0 Y3 0 0 0 Y4 0 0 0 Y5 0 0 0 THETA Y0 Y1 Y2 Y3 Y4 ________ ________ ________ ________ ________ Y0 1 Y1 0 2 Y2 0 0 3 Y3 0 0 0 4 Y4 0 0 0 0 5 Y5 0 0 0 0 0 THETA Y5 ________ Y5 6 ALPHA I S Q ________ ________ ________ 1 19 20 21 BETA I S Q ________ ________ ________ I 0 0 0 S 0 0 0 Q 0 0 0 PSI I S Q ________ ________ ________ I 10 S 11 12 Q 13 14 15 PARAMETER SPECIFICATION FOR LATENT CLASS 4 NU Y0 Y1 Y2 Y3 Y4 ________ ________ ________ ________ ________ 1 0 0 0 0 0 NU Y5 ________ 1 0 LAMBDA I S Q ________ ________ ________ Y0 0 0 0 Y1 0 0 0 Y2 0 0 0 Y3 0 0 0 Y4 0 0 0 Y5 0 0 0 THETA Y0 Y1 Y2 Y3 Y4 ________ ________ ________ ________ ________ Y0 1 Y1 0 2 Y2 0 0 3 Y3 0 0 0 4 Y4 0 0 0 0 5 Y5 0 0 0 0 0 THETA Y5 ________ Y5 6 ALPHA I S Q ________ ________ ________ 1 22 23 24 BETA I S Q ________ ________ ________ I 0 0 0 S 0 0 0 Q 0 0 0 PSI I S Q ________ ________ ________ I 10 S 11 12 Q 13 14 15 PARAMETER SPECIFICATION FOR LATENT CLASS REGRESSION MODEL PART ALPHA(C) C#1 C#2 C#3 C#4 ________ ________ ________ ________ 1 25 26 27 0 STARTING VALUES FOR LATENT CLASS 1 NU Y0 Y1 Y2 Y3 Y4 ________ ________ ________ ________ ________ 1 0.000 0.000 0.000 0.000 0.000 NU Y5 ________ 1 0.000 LAMBDA I S Q ________ ________ ________ Y0 1.000 0.000 0.000 Y1 1.000 0.200 0.040 Y2 1.000 0.400 0.160 Y3 1.000 0.600 0.360 Y4 1.000 0.900 0.810 Y5 1.000 1.200 1.440 THETA Y0 Y1 Y2 Y3 Y4 ________ ________ ________ ________ ________ Y0 5.832 Y1 0.000 10.081 Y2 0.000 0.000 11.830 Y3 0.000 0.000 0.000 12.155 Y4 0.000 0.000 0.000 0.000 12.616 Y5 0.000 0.000 0.000 0.000 0.000 THETA Y5 ________ Y5 12.016 ALPHA I S Q ________ ________ ________ 1 15.896 -17.988 10.275 BETA I S Q ________ ________ ________ I 0.000 0.000 0.000 S 0.000 0.000 0.000 Q 0.000 0.000 0.000 PSI I S Q ________ ________ ________ I 11.807 S 0.000 203.468 Q 0.000 0.000 233.785 STARTING VALUES FOR LATENT CLASS 2 NU Y0 Y1 Y2 Y3 Y4 ________ ________ ________ ________ ________ 1 0.000 0.000 0.000 0.000 0.000 NU Y5 ________ 1 0.000 LAMBDA I S Q ________ ________ ________ Y0 1.000 0.000 0.000 Y1 1.000 0.200 0.040 Y2 1.000 0.400 0.160 Y3 1.000 0.600 0.360 Y4 1.000 0.900 0.810 Y5 1.000 1.200 1.440 THETA Y0 Y1 Y2 Y3 Y4 ________ ________ ________ ________ ________ Y0 5.832 Y1 0.000 10.081 Y2 0.000 0.000 11.830 Y3 0.000 0.000 0.000 12.155 Y4 0.000 0.000 0.000 0.000 12.616 Y5 0.000 0.000 0.000 0.000 0.000 THETA Y5 ________ Y5 12.016 ALPHA I S Q ________ ________ ________ 1 15.896 -17.988 10.275 BETA I S Q ________ ________ ________ I 0.000 0.000 0.000 S 0.000 0.000 0.000 Q 0.000 0.000 0.000 PSI I S Q ________ ________ ________ I 11.807 S 0.000 203.468 Q 0.000 0.000 233.785 STARTING VALUES FOR LATENT CLASS 3 NU Y0 Y1 Y2 Y3 Y4 ________ ________ ________ ________ ________ 1 0.000 0.000 0.000 0.000 0.000 NU Y5 ________ 1 0.000 LAMBDA I S Q ________ ________ ________ Y0 1.000 0.000 0.000 Y1 1.000 0.200 0.040 Y2 1.000 0.400 0.160 Y3 1.000 0.600 0.360 Y4 1.000 0.900 0.810 Y5 1.000 1.200 1.440 THETA Y0 Y1 Y2 Y3 Y4 ________ ________ ________ ________ ________ Y0 5.832 Y1 0.000 10.081 Y2 0.000 0.000 11.830 Y3 0.000 0.000 0.000 12.155 Y4 0.000 0.000 0.000 0.000 12.616 Y5 0.000 0.000 0.000 0.000 0.000 THETA Y5 ________ Y5 12.016 ALPHA I S Q ________ ________ ________ 1 15.896 -17.988 10.275 BETA I S Q ________ ________ ________ I 0.000 0.000 0.000 S 0.000 0.000 0.000 Q 0.000 0.000 0.000 PSI I S Q ________ ________ ________ I 11.807 S 0.000 203.468 Q 0.000 0.000 233.785 STARTING VALUES FOR LATENT CLASS 4 NU Y0 Y1 Y2 Y3 Y4 ________ ________ ________ ________ ________ 1 0.000 0.000 0.000 0.000 0.000 NU Y5 ________ 1 0.000 LAMBDA I S Q ________ ________ ________ Y0 1.000 0.000 0.000 Y1 1.000 0.200 0.040 Y2 1.000 0.400 0.160 Y3 1.000 0.600 0.360 Y4 1.000 0.900 0.810 Y5 1.000 1.200 1.440 THETA Y0 Y1 Y2 Y3 Y4 ________ ________ ________ ________ ________ Y0 5.832 Y1 0.000 10.081 Y2 0.000 0.000 11.830 Y3 0.000 0.000 0.000 12.155 Y4 0.000 0.000 0.000 0.000 12.616 Y5 0.000 0.000 0.000 0.000 0.000 THETA Y5 ________ Y5 12.016 ALPHA I S Q ________ ________ ________ 1 15.896 -17.988 10.275 BETA I S Q ________ ________ ________ I 0.000 0.000 0.000 S 0.000 0.000 0.000 Q 0.000 0.000 0.000 PSI I S Q ________ ________ ________ I 11.807 S 0.000 203.468 Q 0.000 0.000 233.785 STARTING VALUES FOR LATENT CLASS REGRESSION MODEL PART ALPHA(C) C#1 C#2 C#3 C#4 ________ ________ ________ ________ 1 0.000 0.000 0.000 0.000 SAMPLE STATISTICS FOR ESTIMATED FACTOR SCORES SAMPLE STATISTICS Means I S Q C_I C_S ________ ________ ________ ________ ________ 1 16.180 -16.615 8.376 16.128 -16.589 Means C_Q ________ 1 7.987 Covariances I S Q C_I C_S ________ ________ ________ ________ ________ I 5.510 S 3.669 48.323 Q -2.670 -29.525 20.673 C_I 5.587 3.859 -2.774 5.711 C_S 4.213 47.491 -29.018 4.441 50.205 C_Q -2.530 -27.005 19.290 -2.456 -28.552 Covariances C_Q ________ C_Q 20.392 Correlations I S Q C_I C_S ________ ________ ________ ________ ________ I 1.000 S 0.225 1.000 Q -0.250 -0.934 1.000 C_I 0.996 0.232 -0.255 1.000 C_S 0.253 0.964 -0.901 0.262 1.000 C_Q -0.239 -0.860 0.940 -0.228 -0.892 Correlations C_Q ________ C_Q 1.000 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: 11:56:34 Ending Time: 12:00:00 Elapsed Time: 00:03:26 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) 1998-2010 Muthen & Muthen