Mplus Users Guide v6.. - Muthén & Muthén

More documents

Recommendations

Info

CHAPTER 6the outcome variable. Two growth factors are used in the model, arandom intercept, i, and a random slope, s.In the parameterization of the growth model shown here, the interceptsof the outcome variables are fixed at zero as the default. The residualvariances of the outcome variables are free to be estimated as thedefault. The residual covariances of the outcome variables are fixed atzero as the default. The means, variances, and covariances of theintercept and slope growth factors are free as the default.The second, third, fourth, and fifth | statements use the ON option toname and define the random slope variables in the model. The name onthe left-hand side of the | symbol names the random slope variable. Thestatement on the right-hand side of the | symbol defines the random slopevariable. In the second | statement, the random slope st is defined by thelinear regression of the dependent variable y1 on the time-varyingcovariate a21. In the third | statement, the random slope st is defined bythe linear regression of the dependent variable y2 on the time-varyingcovariate a22. In the fourth | statement, the random slope st is definedby the linear regression of the dependent variable y3 on the time-varyingcovariate a23. In the fifth | statement, the random slope st is defined bythe linear regression of the dependent variable y4 on the time-varyingcovariate a24. Random slopes with the same name are treated as onevariable during model estimation. The ON statement describes the linearregressions of the intercept growth factor i, the slope growth factor s,and the random slope st on the covariate x. The intercepts and residualvariances of, i, s, and st, are free as the default. The residual covariancebetween i and s is estimated as the default. The residual covariancesbetween st and i and s are fixed at zero as the default. The defaultestimator for this type of analysis is maximum likelihood with robuststandard errors. The estimator option of the ANALYSIS command canbe used to select a different estimator. An explanation of the othercommands can be found in Example 6.1.118
Examples: Growth Modeling And Survival AnalysisEXAMPLE 6.13: GROWTH MODEL FOR TWO PARALLELPROCESSES FOR CONTINUOUS OUTCOMES WITHREGRESSIONS AMONG THE RANDOM EFFECTSTITLE: this is an example of a growth model fortwo parallel processes for continuousoutcomes with regressions among the randomeffectsDATA: FILE IS ex6.13.dat;VARIABLE: NAMES ARE y11 y12 y13 y14 y21 y22 y23 y24;MODEL: i1 s1 | y11@0 y12@1 y13@2 y14@3;i2 s2 | y21@0 y22@1 y23@2 y24@3;s1 ON i2;s2 ON i1;y11 y12 y13 y14ilsli2s2y21 y22 y23 y24119
Page 1 and 2:
Statistical Analysis With Latent Va
Page 3 and 4:
TABLE OF CONTENTSChapter 1: Introdu
Page 7 and 8:
IntroductionCHAPTER 1INTRODUCTIONMp
Page 10:
CHAPTER 1• Regression analysis•
Page 14 and 15:
CHAPTER 1When there are no covariat
Page 16 and 17:
CHAPTER 1model. For example, variab
Page 18 and 19:
CHAPTER 112
Page 20 and 21:
CHAPTER 2details of the analysis. T
Page 22 and 23:
CHAPTER 2TITLE: this is an example
Page 24 and 25:
CHAPTER 2The Mplus Base and Multile
Page 26 and 27:
CHAPTER 3• Wald chi-square test o
Page 28 and 29:
CHAPTER 3EXAMPLE 3.1: LINEAR REGRES
Page 30 and 31:
CHAPTER 3EXAMPLE 3.3: CENSORED-INFL
Page 32 and 33:
CHAPTER 3example above, u1 is a bin
Page 34 and 35:
CHAPTER 3EXAMPLE 3.8: ZERO-INFLATED
Page 36 and 37:
CHAPTER 3x1yx2sIn this example a re
Page 38 and 39:
CHAPTER 3When two parameters are re
Page 40 and 41:
CHAPTER 3logistic regressions. An e
Page 42 and 43:
CHAPTER 3EXAMPLE 3.15: PATH ANALYSI
Page 44 and 45:
CHAPTER 3The difference between thi
Page 46 and 47:
CHAPTER 3The TECH8 option is used t
Page 48 and 49:
CHAPTER 4CLUSTER, and WEIGHT option
Page 50 and 51:
CHAPTER 4ANALYSIS: TYPE = EFA 1 4;T
Page 52 and 53:
CHAPTER 4EXAMPLE 4.3: EXPLORATORY F
Page 54 and 55:
CHAPTER 4ANALYSIS command is used t
Page 56 and 57:
CHAPTER 4indicators. Rotated soluti
Page 58 and 59:
CHAPTER 5and a set of Poisson or ze
Page 60 and 61:
CHAPTER 5Following is the set of CF
Page 62 and 63:
CHAPTER 5y1f1y2y3y4f2y5y6In this ex
Page 64 and 65:
CHAPTER 5above, all six factor indi
Page 66 and 67:
CHAPTER 5computationally demanding
Page 68 and 69:
CHAPTER 5y1 y2 y3 y4y5 y6 y7 y8y9 y
Page 70 and 71:
CHAPTER 5EXAMPLE 5.8: CFA WITH COVA
Page 72 and 73:
CHAPTER 5y1af1y1by1cy2af2y2by2cIn t
Page 74 and 75: CHAPTER 5that the three test forms
Page 76 and 77: CHAPTER 5EXAMPLE 5.12: SEM WITH CON
Page 78 and 79: CHAPTER 5interaction is shown in th
Page 80 and 81: CHAPTER 5In multiple group analysis
Page 82 and 83: CHAPTER 5square brackets. When a mo
Page 84 and 85: CHAPTER 5EXAMPLE 5.18: TWO-GROUP TW
Page 88 and 89: CHAPTER 5EXAMPLE 5.20: CFA WITH PAR
Page 92 and 93: CHAPTER 5unit variance of the laten
Page 94 and 95: CHAPTER 5EXAMPLE 5.24: EFA WITH COV
Page 96 and 97: CHAPTER 5y7 y8 y9 y10 y11 y12y1y2f1
Page 98 and 99: CHAPTER 5y1 y2 y3 y4y5 y6 y7 y8y9 y
Page 100 and 101: CHAPTER 5In this example, the multi
Page 102 and 103: CHAPTER 5MODEL: f1-f2 by y1-y10 (*1
Page 104 and 105: CHAPTER 6slopes are also used to re
Page 106 and 107: CHAPTER 6for each graphical display
Page 108 and 109: CHAPTER 6In this example, the linea
Page 110 and 111: CHAPTER 6be different across time a
Page 112 and 113: CHAPTER 6name of the censored varia
Page 114 and 115: CHAPTER 6outcomes with a numerical
Page 116 and 117: CHAPTER 6EXAMPLE 6.7: LINEAR GROWTH
Page 118 and 119: CHAPTER 6EXAMPLE 6.8: GROWTH MODEL
Page 120: CHAPTER 6EXAMPLE 6.10: LINEAR GROWT
Page 123: Examples: Growth Modeling And Survi
Page 127 and 128: Examples: Growth Modeling And Survi
Page 134 and 135: CHAPTER 6EXAMPLE 6.17: LINEAR GROWT
Page 136 and 137: CHAPTER 6y1 y2 y3 y4isx a21 a22 a23
Page 138 and 139: CHAPTER 6The GROUPING option is use
Page 140 and 141: CHAPTER 6occurred, and a missing va
Page 142 and 143: CHAPTER 6In this example, the conti
Page 144 and 145: CHAPTER 6intervals plus one. These
Page 146 and 147: CHAPTER 6140
Page 148 and 149: CHAPTER 7zero-inflated Poisson regr
Page 150 and 151: CHAPTER 7Graphical displays of obse
Page 152 and 153: CHAPTER 7EXAMPLE 7.1: MIXTURE REGRE
Page 154 and 155: CHAPTER 7is to be performed. By sel
Page 156 and 157: CHAPTER 7EXAMPLE 7.2: MIXTURE REGRE
Page 158 and 159: CHAPTER 7u1u2cu3u4In this example,
Page 160 and 161: CHAPTER 7In the MODEL command, user
Page 162 and 163: CHAPTER 7The difference between thi
Page 164 and 165: CHAPTER 7EXAMPLE 7.9: LCA WITH CONT
Page 166 and 167: CHAPTER 7indicators, the default co
Page 168 and 169: CHAPTER 7EXAMPLE 7.12: LCA WITH BIN
Page 170 and 171: CHAPTER 7The first hypothesis is sp
Page 172 and 173: CHAPTER 7The CLASSES option is used
Page 174 and 175:
CHAPTER 7between the first two vari
Page 176 and 177:
CHAPTER 7EXAMPLE 7.17: CFA MIXTURE
Page 178 and 179:
CHAPTER 7u11 u12 u13u21 u22 u23c1c2
Page 180 and 181:
CHAPTER 7In this example, the model
Page 182 and 183:
CHAPTER 7EXAMPLE 7.21: MIXTURE MODE
Page 184 and 185:
CHAPTER 7y1y2cy3y4In this example,
Page 186 and 187:
CHAPTER 7ycx1x2In this example, the
Page 188 and 189:
CHAPTER 7MODEL:%OVERALL%y ON x1 x2;
Page 190 and 191:
CHAPTER 7In this example, the zero-
Page 192 and 193:
CHAPTER 7EXAMPLE 7.27: FACTOR MIXTU
Page 194 and 195:
CHAPTER 7EXAMPLE 7.28: TWO-GROUP TW
Page 196 and 197:
CHAPTER 7algorithm will be used. No
Page 198 and 199:
CHAPTER 7u11 u12 u13 u14u21 u22 u23
Page 200 and 201:
CHAPTER 7MODEL:OUTPUT:PLOT:%OVERALL
Page 202 and 203:
CHAPTER 7196
Page 204 and 205:
CHAPTER 8All longitudinal mixture m
Page 206 and 207:
CHAPTER 8• 8.8: GMM with known cl
Page 208 and 209:
CHAPTER 8to the growth factors i an
Page 210 and 211:
CHAPTER 8estimated as the default,
Page 212 and 213:
CHAPTER 8EXAMPLE 8.3: GMM FOR A CEN
Page 214 and 215:
CHAPTER 8algorithm will be used. No
Page 216 and 217:
CHAPTER 8by adding to the name of t
Page 218 and 219:
CHAPTER 8y1 y2 y3 y4isx c uThe diff
Page 220 and 221:
CHAPTER 8y1 y2 y3 y4 y5 y6 y7 y8i1
Page 222 and 223:
CHAPTER 8where c2#1 refers to the f
Page 224 and 225:
CHAPTER 8parts of the model, starti
Page 226 and 227:
CHAPTER 8The difference between thi
Page 228 and 229:
CHAPTER 8MODEL c1:MODEL c2:MODEL c3
Page 230 and 231:
Page 232 and 233:
CHAPTER 8MODEL c.c1:%c#1.c1#1%[u11$
Page 234 and 235:
CHAPTER 8used to select a different
Page 236 and 237:
CHAPTER 8ALGORITHM=INTEGRATION as i
Page 238 and 239:
CHAPTER 8ON statement describes the
Page 240 and 241:
CHAPTER 9VARIABLE command. Observed
Page 242 and 243:
CHAPTER 9for individual data or the
Page 244 and 245:
CHAPTER 9• 9.16: Linear growth mo
Page 246 and 247:
CHAPTER 9TITLE:this is an example o
Page 248 and 249:
CHAPTER 9Following is the second pa
Page 250 and 251:
CHAPTER 9xsyWithinwyBetweenxmsThe d
Page 252 and 253:
CHAPTER 9EXAMPLE 9.3: TWO-LEVEL PAT
Page 254 and 255:
CHAPTER 9integration are used with
Page 256 and 257:
CHAPTER 9EXAMPLE 9.5: TWO-LEVEL PAT
Page 258 and 259:
CHAPTER 9using the ON option. In th
Page 260 and 261:
CHAPTER 9constrained to be equal ac
Page 262 and 263:
CHAPTER 9does not require numerical
Page 264 and 265:
CHAPTER 9In the within part of the
Page 266 and 267:
CHAPTER 9u1x1fw1u2u3u4x2fw2u5u6With
Page 268 and 269:
CHAPTER 9In the between part of the
Page 270 and 271:
CHAPTER 9y1y2fwsy5y3y4Withiny1Betwe
Page 272 and 273:
CHAPTER 9the factor fb and the clus
Page 274 and 275:
CHAPTER 9and individuals with g equ
Page 276 and 277:
CHAPTER 9y1y2y3y4iwswxWithinBetween
Page 278 and 279:
CHAPTER 9residual variances of the
Page 280 and 281:
CHAPTER 9EXAMPLE 9.14: TWO-LEVEL GR
Page 282 and 283:
CHAPTER 9as y1, y2, y3, and y4 in t
Page 284 and 285:
CHAPTER 9u11u21u31u12 u22 u32 u13 u
Page 286 and 287:
CHAPTER 9statements are fixed at ze
Page 288 and 289:
CHAPTER 9timesya3Withinx1yBetweenx2
Page 290 and 291:
CHAPTER 9EXAMPLE 9.17: TWO-LEVEL GR
Page 292 and 293:
CHAPTER 9model, the variances of th
Page 294 and 295:
CHAPTER 9288
Page 296 and 297:
CHAPTER 10• Complex survey data
Page 298 and 299:
CHAPTER 10• 10.13: Two-level LTA
Page 300 and 301:
CHAPTER 10across clusters. The rand
Page 302 and 303:
CHAPTER 10refers to the part of the
Page 304 and 305:
CHAPTER 10EXAMPLE 10.2: TWO-LEVEL M
Page 306 and 307:
CHAPTER 10the end of the arrow poin
Page 308 and 309:
CHAPTER 10EXAMPLE 10.3: TWO-LEVEL M
Page 310 and 311:
CHAPTER 10random intercept y is sho
Page 312 and 313:
CHAPTER 10y1 y2 y3 y4y5cfwWithinBet
Page 314 and 315:
CHAPTER 10EXAMPLE 10.5: TWO-LEVEL I
Page 316 and 317:
CHAPTER 10latent variable c. The fi
Page 318 and 319:
CHAPTER 10u1 u2 u3 u4u5u6cxWithinBe
Page 320 and 321:
CHAPTER 10EXAMPLE 10.7: TWO-LEVEL L
Page 322 and 323:
CHAPTER 10In the overall part of th
Page 324 and 325:
CHAPTER 10y1 y2 y3 y4iwsswxWithinBe
Page 326 and 327:
CHAPTER 10Following is an alternati
Page 328 and 329:
CHAPTER 10y1 y2 y3 y4iwswcxWithinBe
Page 330 and 331:
CHAPTER 10coefficients of the inter
Page 332 and 333:
CHAPTER 10y1 y2 y3 y4iwswcxWithinBe
Page 334 and 335:
CHAPTER 10EXAMPLE 10.11: TWO-LEVEL
Page 336 and 337:
CHAPTER 10In the overall part of th
Page 338 and 339:
CHAPTER 10In this example, the two-
Page 340 and 341:
Page 342 and 343:
CHAPTER 10336
Page 344 and 345:
CHAPTER 11With Bayesian analysis, m
Page 346 and 347:
CHAPTER 11number of parameters and
Page 348 and 349:
CHAPTER 11EXAMPLE 11.2: DESCRIPTIVE
Page 350 and 351:
CHAPTER 11MODEL:OUTPUT:i s | y0@0 y
Page 352 and 353:
CHAPTER 11y0 y1 y2 y3 y4 y5i s qd1
Page 354 and 355:
CHAPTER 11analysis. In the second p
Page 356 and 357:
CHAPTER 11DATA IMPUTATION:NDATASETS
Page 358 and 359:
CHAPTER 11MODEL: %WITHIN%f1w BY u11
Page 360 and 361:
CHAPTER 11TITLE:DATA:this is an exa
Page 362 and 363:
CHAPTER 11The ANALYSIS command is u
Page 364 and 365:
CHAPTER 12saved in an external file
Page 366 and 367:
CHAPTER 12• 12.11: Monte Carlo si
Page 368 and 369:
CHAPTER 12MONTE CARLO OUTPUTChi-Squ
Page 370 and 371:
CHAPTER 12The column labeled Popula
Page 372 and 373:
CHAPTER 12In this example, data are
Page 374 and 375:
CHAPTER 12covariances of the indepe
Page 376 and 377:
CHAPTER 12slope factor s. The binar
Page 378 and 379:
CHAPTER 12ANALYSIS: TYPE = MIXTURE;
Page 380 and 381:
CHAPTER 12MODEL MISSING:[y1-y4@-1];
Page 382 and 383:
CHAPTER 12The default estimator for
Page 384 and 385:
CHAPTER 12EXAMPLE 12.6 STEP 2: EXTE
Page 386 and 387:
CHAPTER 12EXAMPLE 12.7 STEP 2: MONT
Page 388 and 389:
CHAPTER 12MODEL MISSING command spe
Page 390 and 391:
CHAPTER 12MODEL:OUTPUT:iu su | u1@0
Page 392 and 393:
CHAPTER 12continuous-time survival
Page 394 and 395:
CHAPTER 12MODEL:%WITHIN%c | y ON x;
Page 396 and 397:
CHAPTER 12invariance across groups
Page 398 and 399:
CHAPTER 13EXAMPLE 13.1: A COVARIANC
Page 400 and 401:
CHAPTER 13EXAMPLE 13.4: NON-NUMERIC
Page 402 and 403:
CHAPTER 13EXAMPLE 13.7: TRANSFORMIN
Page 404 and 405:
CHAPTER 13EXAMPLE 13.10: EQUALITIES
Page 406 and 407:
CHAPTER 13TITLE: this is an example
Page 408 and 409:
CHAPTER 13EXAMPLE 13.14: SAVING DAT
Page 410 and 411:
CHAPTER 13EXAMPLE 13.17: MERGING DA
Page 412 and 413:
CHAPTER 13EXAMPLE 13.19: GENERATING
Page 414 and 415:
CHAPTER 14• User-specified starti
Page 416 and 417:
CHAPTER 14conjunction with the | sy
Page 418 and 419:
CHAPTER 14Loadings for indicators o
Page 420 and 421:
CHAPTER 14possible that a local sol
Page 422 and 423:
CHAPTER 14GENERAL CONVERGENCE PROBL
Page 424 and 425:
CHAPTER 14Model identification can
Page 426 and 427:
CHAPTER 14computations will become
Page 428 and 429:
CHAPTER 14covariances and regressio
Page 430 and 431:
CHAPTER 14MODEL COMMAND IN MULTIPLE
Page 432 and 433:
CHAPTER 14in the other two groups.
Page 434 and 435:
CHAPTER 14MODEL g2: y1-y5 (2);MODEL
Page 436 and 437:
CHAPTER 14variances in a model with
Page 438 and 439:
CHAPTER 1432 32 2 32 2 2 3where the
Page 440 and 441:
CHAPTER 14WEIGHTED LEAST SQUARES ES
Page 442 and 443:
CHAPTER 14Multiple data sets genera
Page 444 and 445:
CHAPTER 14Observation Cohort HD82 H
Page 446 and 447:
CHAPTER 14CALCULATING PROBABILITIES
Page 448 and 449:
CHAPTER 14log odds (x 0 +1) = a + b
Page 450 and 451:
CHAPTER 14values are exponentiated
Page 452 and 453:
CHAPTER 14available for these model
Page 454 and 455:
CHAPTER 14The joint probabilities f
Page 456 and 457:
CHAPTER 15THE DATA COMMANDThe DATA
Page 458 and 459:
CHAPTER 15DATA LONGTOWIDE:LONG =WID
Page 460 and 461:
CHAPTER 15to the number of variable
Page 462 and 463:
CHAPTER 15TYPEThe TYPE option is us
Page 464 and 465:
CHAPTER 15and saved using another c
Page 466 and 467:
CHAPTER 15LISTWISE = ON;SWMATRIXThe
Page 468 and 469:
CHAPTER 15parentheses following its
Page 470 and 471:
CHAPTER 15uses a model of unrestric
Page 472 and 473:
CHAPTER 15WIDEThe WIDE option is us
Page 474 and 475:
CHAPTER 15LONG = y | x;where y and
Page 476 and 477:
CHAPTER 15categorical using the CAT
Page 478 and 479:
CHAPTER 15THE DATA MISSING COMMANDT
Page 480 and 481:
CHAPTER 153. The value zero is assi
Page 482 and 483:
CHAPTER 15NAMESThe NAMES option ide
Page 484 and 485:
CHAPTER 15cohort. In the example ab
Page 486 and 487:
CHAPTER 15THE VARIABLE COMMANDVARIA
Page 488 and 489:
CHAPTER 15ASSIGNING NAMES TO VARIAB
Page 490 and 491:
CHAPTER 15The USEVARIABLES option i
Page 492 and 493:
CHAPTER 15MISSING ARE ethnic (9 99)
Page 494 and 495:
CHAPTER 15CATEGORICALThe CATEGORICA
Page 496 and 497:
CHAPTER 15where the set of variable
Page 498 and 499:
CHAPTER 15COUNT = u1-u4 (p);The COU
Page 500 and 501:
CHAPTER 15GROUPINGThe GROUPING opti
Page 502 and 503:
CHAPTER 15TSCORESThe TSCORES option
Page 504 and 505:
CHAPTER 15parentheses is placed beh
Page 506 and 507:
CHAPTER 15the VARIABLE command and
Page 508 and 509:
CHAPTER 15WTSCALEThe WTSCALE option
Page 510 and 511:
CHAPTER 15ANALYSIS command. The sam
Page 512 and 513:
CHAPTER 15MIXTURE MODELSThere are t
Page 514 and 515:
CHAPTER 15Fractional values can be
Page 516 and 517:
CHAPTER 15CONTINUOUS-TIME SURVIVAL
Page 518 and 519:
CHAPTER 15THE DEFINE COMMANDDEFINE:
Page 520 and 521:
CHAPTER 15The _MISSING keyword can
Page 522 and 523:
CHAPTER 15where the variable ymean
Page 524 and 525:
CHAPTER 15518
Page 526 and 527:
CHAPTER 16WLS;WLSM;WLSMV;ULS;ULSMV;
Page 528 and 529:
CHAPTER 16STSCALE = random start sc
Page 530 and 531:
CHAPTER 16The ANALYSIS command is n
Page 532 and 533:
CHAPTER 16TYPE = GENERAL RANDOM;or
Page 534 and 535:
CHAPTER 16• COMPLEX computes stan
Page 536 and 537:
CHAPTER 16where the first two numbe
Page 538 and 539:
CHAPTER 16TWOLEVELMUML***ML**MLR**M
Page 540 and 541:
CHAPTER 16BAYESIAN ESTIMATIONBayesi
Page 542 and 543:
CHAPTER 16LOGIT REGRESSION VERSUS L
Page 544 and 545:
CHAPTER 16where .5 is the value of
Page 546 and 547:
CHAPTER 16ROTATION = TARGET (ORTHOG
Page 548 and 549:
CHAPTER 16For the FAY resampling me
Page 550 and 551:
CHAPTER 16OPTIONS RELATED TO NUMERI
Page 552 and 553:
CHAPTER 16For other outcome types a
Page 554 and 555:
CHAPTER 16BOOTSTRAPThe BOOTSTRAP op
Page 556 and 557:
CHAPTER 16values produced by the pr
Page 558 and 559:
CHAPTER 16OPTSEEDThe OPTSEED option
Page 560 and 561:
CHAPTER 16DIFFTEST = deriv.dat;wher
Page 562 and 563:
CHAPTER 16RITERATIONSThe RITERATION
Page 564 and 565:
CHAPTER 16MIXUThe MIXU option is us
Page 566 and 567:
CHAPTER 16With multiple chains, par
Page 568 and 569:
CHAPTER 16such that convergence is
Page 570 and 571:
CHAPTER 16skipped. A setting of 1 s
Page 572 and 573:
CHAPTER 16566
Page 574 and 575:
CHAPTER 17DEPENDENT OR INDEPENDENT
Page 576 and 577:
CHAPTER 17| names and defines rando
Page 578 and 579:
CHAPTER 17short for measured by. ON
Page 580 and 581:
CHAPTER 17because BY statements are
Page 582 and 583:
CHAPTER 17allowed with TYPE=GENERAL
Page 584 and 585:
CHAPTER 172001). The target factor
Page 586 and 587:
CHAPTER 17specifies that regression
Page 588 and 589:
CHAPTER 17PON, the number of variab
Page 590 and 591:
CHAPTER 17impliesy1 WITH y4;y1 WITH
Page 592 and 593:
CHAPTER 17refers to the means of va
Page 594 and 595:
CHAPTER 17f1 ON x1 x2 x3;f2 ON x1 x
Page 596 and 597:
CHAPTER 17By placing an asterisk (*
Page 598 and 599:
CHAPTER 17possible thresholds, thre
Page 600 and 601:
CHAPTER 17A list of equality constr
Page 602 and 603:
CHAPTER 17parameters. Following is
Page 604 and 605:
CHAPTER 17Although y5 is assigned a
Page 606 and 607:
CHAPTER 17LABELING CLASSES OF A CAT
Page 608 and 609:
CHAPTER 17[y1-y4@0 i s q];If the |
Page 610 and 611:
CHAPTER 17Linear for acount outcome
Page 612 and 613:
CHAPTER 17MultilevelMultipleindicat
Page 614 and 615:
CHAPTER 17In mixture models for con
Page 616 and 617:
CHAPTER 17ATThe AT option is used w
Page 618 and 619:
CHAPTER 17Numerical integration bec
Page 620 and 621:
CHAPTER 17INDThe variable on the le
Page 622 and 623:
CHAPTER 17PARAMETERS LABELLED IN TH
Page 624 and 625:
CHAPTER 17right-hand side of one or
Page 626 and 627:
CHAPTER 17Type of Parameter Distrib
Page 628 and 629:
CHAPTER 17number gives the number o
Page 630 and 631:
CHAPTER 17These are used to specify
Page 632 and 633:
CHAPTER 17so on. In mixture modelin
Page 634 and 635:
CHAPTER 17In mixture modeling, the
Page 636 and 637:
CHAPTER 17A dependent variable that
Page 638 and 639:
CHAPTER 17632
Page 640 and 641:
CHAPTER 18CINTERVAL;CINTERVAL (SYMM
Page 642 and 643:
CHAPTER 18Following is the summary
Page 644 and 645:
CHAPTER 18and thresholds are found
Page 646 and 647:
CHAPTER 18printed is too large to f
Page 648 and 649:
CHAPTER 18where b is the unstandard
Page 650 and 651:
CHAPTER 18the third column. When st
Page 652 and 653:
CHAPTER 18When model modification i
Page 654 and 655:
CHAPTER 18CONFIDENCE INTERVALS OF M
Page 656 and 657:
CHAPTER 18NOSERROR;This option is n
Page 658 and 659:
CHAPTER 18FACTOR DETERMINACIESTECHN
Page 660 and 661:
CHAPTER 18THETAY1 Y2 Y3 Y4 X_______
Page 662 and 663:
CHAPTER 18TECHNICAL 4 OUTPUTTECH4Th
Page 664 and 665:
CHAPTER 18model with one less class
Page 666 and 667:
CHAPTER 18of draws varies from 2 to
Page 668 and 669:
CHAPTER 18GAMMAThe gamma matrix con
Page 670 and 671:
CHAPTER 18• Analysis data• Samp
Page 672 and 673:
CHAPTER 18by the program, the recod
Page 674 and 675:
CHAPTER 18SIGBETWEENThe SIGBETWEEN
Page 676 and 677:
CHAPTER 18using the POPULATION and/
Page 678 and 679:
CHAPTER 18SURVIVAL option. Followin
Page 680 and 681:
CHAPTER 18FSCORESWhen SAVE=FSCORES
Page 682 and 683:
CHAPTER 18COOKSWhen SAVE=COOKS is u
Page 684 and 685:
CHAPTER 18For data in fixed format,
Page 686 and 687:
CHAPTER 18• Sample proportions•
Page 688 and 689:
CHAPTER 18Following is an example o
Page 690 and 691:
CHAPTER 18VIEWING GRAPHICAL OUTPUTS
Page 692 and 693:
CHAPTER 18Following is the window t
Page 694 and 695:
CHAPTER 18The plots can be exported
Page 696 and 697:
CHAPTER 19CLASSES =SURVIVAL =TSCORE
Page 698 and 699:
CHAPTER 19For Monte Carlo studies,
Page 700 and 701:
CHAPTER 19binomial model. The lette
Page 702 and 703:
CHAPTER 19where c1, c2, and c3 are
Page 704 and 705:
CHAPTER 19together to generate miss
Page 706 and 707:
CHAPTER 19between dependent and ind
Page 708 and 709:
CHAPTER 19categories are referred t
Page 710 and 711:
CHAPTER 19Following is the specific
Page 712 and 713:
CHAPTER 19TSCORESThe TSCORES option
Page 714 and 715:
CHAPTER 19where estimates.dat is a
Page 716 and 717:
CHAPTER 19710
Page 718 and 719:
CHAPTER 20DATA IMPUTATION:IMPUTE =N
Page 720 and 721:
CHAPTER 20TSCORES AREnames of obser
Page 722 and 723:
CHAPTER 20ESTIMATOR = ML; depends o
Page 724 and 725:
CHAPTER 20STCONVERGENCE = initial s
Page 726 and 727:
CHAPTER 20INTERACTIVE =PROCESSORS =
Page 728 and 729:
CHAPTER 20MODEL COVERAGE:%WITHIN%%B
Page 730 and 731:
CHAPTER 20TYPE IS COVARIANCE; varie
Page 732 and 733:
CHAPTER 20COVERAGE =STARTING =REPSA
Page 734 and 735:
Asparouhov, T. & Muthén, B. (2009a
Page 736 and 737:
Hagenaars, J.A. & McCutcheon, A.L.
Page 738 and 739:
Larsen, K. (2005). The Cox proporti
Page 740 and 741:
Muthén, B. (2002). Beyond SEM: Gen
Page 742 and 743:
Qu, Y., Tan, M., & Kutner, M.H. (19
Page 744 and 745:
738
Page 746 and 747:
CATEGORICALMonte Carlo, 701real dat
Page 748 and 749:
SEM with EFA and CFA factors, 89-91
Page 750 and 751:
inary outcome, 218-19three-category
Page 752 and 753:
two-level growth mixture model (GMM
Page 754 and 755:
PRIORS, 507-8PROBABILITIES, 507-8pr
Page 756 and 757:
TIMEMEASURES, 478-79time-to-event v
Page 758:
MUTHÉN & MUTHÉNMplus SINGLE-USER
show all

Mplus Users Guide v6.. - Muthén & Muthén

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?