Mplus Users Guide v6.. - Muthén & Muthén

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CHAPTER 6EXAMPLE 6.10: LINEAR GROWTH MODEL FOR ACONTINUOUS OUTCOME WITH TIME-INVARIANT ANDTIME-VARYING COVARIATESTITLE: this is an example of a linear growthmodel for a continuous outcome with timeinvariantand time-varying covariatesDATA: FILE IS ex6.10.dat;VARIABLE: NAMES ARE y11-y14 x1 x2 a31-a34;MODEL: i s | y11@0 y12@1 y13@2 y14@3;i s ON x1 x2;y11 ON a31;y12 ON a32;y13 ON a33;y14 ON a34;y11 y12 y13 y14isx1 x2 a31 a32 a33 a34The difference between this example and Example 6.1 is that timeinvariantand time-varying covariates as shown in the picture above areincluded in the model.114
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Statistical Analysis With Latent Va
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TABLE OF CONTENTSChapter 1: Introdu
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IntroductionCHAPTER 1INTRODUCTIONMp
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CHAPTER 1• Regression analysis•
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CHAPTER 1When there are no covariat
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CHAPTER 1model. For example, variab
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CHAPTER 112
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CHAPTER 2details of the analysis. T
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CHAPTER 2TITLE: this is an example
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CHAPTER 2The Mplus Base and Multile
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CHAPTER 3• Wald chi-square test o
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CHAPTER 3EXAMPLE 3.1: LINEAR REGRES
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CHAPTER 3EXAMPLE 3.3: CENSORED-INFL
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CHAPTER 3example above, u1 is a bin
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CHAPTER 3EXAMPLE 3.8: ZERO-INFLATED
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CHAPTER 3x1yx2sIn this example a re
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CHAPTER 3When two parameters are re
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CHAPTER 3logistic regressions. An e
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CHAPTER 3EXAMPLE 3.15: PATH ANALYSI
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CHAPTER 3The difference between thi
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CHAPTER 3The TECH8 option is used t
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CHAPTER 4CLUSTER, and WEIGHT option
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CHAPTER 4ANALYSIS: TYPE = EFA 1 4;T
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CHAPTER 4EXAMPLE 4.3: EXPLORATORY F
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CHAPTER 4ANALYSIS command is used t
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CHAPTER 4indicators. Rotated soluti
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CHAPTER 5and a set of Poisson or ze
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CHAPTER 5Following is the set of CF
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CHAPTER 5y1f1y2y3y4f2y5y6In this ex
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CHAPTER 5above, all six factor indi
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CHAPTER 5computationally demanding
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CHAPTER 5y1 y2 y3 y4y5 y6 y7 y8y9 y
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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 122 and 123: CHAPTER 6one intercept growth facto
Page 124 and 125: CHAPTER 6the outcome variable. Two
Page 126 and 127: CHAPTER 6In this example, the model
Page 128 and 129: CHAPTER 6y33. The metric of the thr
Page 130 and 131: CHAPTER 6The growth model requires
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Examples: Mixture Modeling With Cro
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Examples: Mixture Modeling With Lon
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Examples: Multilevel Modeling With
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Examples: Multilevel Mixture Modeli
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Examples: Missing Data Modeling And
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Examples: Monte Carlo Simulation St
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Examples: Special FeaturesCHAPTER 1
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Examples: Special FeaturesThe examp
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Examples: Special Featuressingle va
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Examples: Special Featureswith star
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Examples: Special FeaturesEXAMPLE 1
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Examples: Special FeaturesEXAMPLE 1
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Examples: Special Featuresformat as
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Examples: Special Featuresflag. If
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Special Modeling IssuesCHAPTER 14SP
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Special Modeling Issuesgroup and la
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Special Modeling Issuesfree regress
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Special Modeling Issuesclass indica
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Special Modeling Issuesextracted. I
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Special Modeling Issuesof the MODEL
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Special Modeling IssuesScale ofDepe
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Special Modeling Issuesfactor analy
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Special Modeling Issueslisted in th
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Special Modeling Issuesof each fact
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Special Modeling Issuesequal to eac
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Special Modeling IssuesThe followin
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Special Modeling Issuesthe grouping
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Special Modeling Issuesinvariance.
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Special Modeling IssuesMISSING DATA
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Special Modeling Issuesbe missing v
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Special Modeling IssuesObs Coh HD17
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Special Modeling Issuesgiven x usin
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Special Modeling Issues(2) P (u = r
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Special Modeling IssuesIn the outpu
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Special Modeling Issuesc1c21 2 31 a
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TITLE, DATA, VARIABLE, And DEFINE C
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ANALYSIS CommandCHAPTER 16ANALYSIS
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ANALYSIS CommandROWSTANDARDIZATION
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ANALYSIS CommandRCONVERGENCE = conv
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ANALYSIS Commandcategorical (nomina
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ANALYSIS Commandaddition, for regre
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ANALYSIS CommandEFAAnalyses using T
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ANALYSIS CommandType of AnalysisTYP
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ANALYSIS CommandFollowing is a desc
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ANALYSIS Commandleast one observed
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ANALYSIS CommandThe following rotat
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ANALYSIS Commandrotation for the CR
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ANALYSIS CommandWITH option of the
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ANALYSIS CommandALGORITHMThe ALGORI
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ANALYSIS CommandMCSEEDThe MCSEED op
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ANALYSIS CommandCOMPLEX TWOLEVEL ML
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ANALYSIS Commandwhere 500 is the nu
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ANALYSIS CommandSTARTS = 100 10;orS
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ANALYSIS Commandwhich specifies tha
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ANALYSIS Commandwhere the value 0 s
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ANALYSIS CommandMCONVERGENCEThe MCO
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ANALYSIS CommandMATRIXThe MATRIX op
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ANALYSIS Commandare imputed using a
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ANALYSIS CommandThe default is 1 in
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ANALYSIS CommandIn addition to para
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MODEL CommandCHAPTER 17MODEL COMMAN
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MODEL CommandTHE MODEL COMMANDThe M
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MODEL CommandMODEL COVERAGE:MODEL C
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MODEL Commandx1y1x2y2x3f1y3x4y4x5y5
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MODEL Commandwhere the asterisk (*)
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MODEL Commandspecifies that factors
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MODEL CommandFor continuous latent
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MODEL CommandFollowing is a table t
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MODEL CommandSeveral variables can
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MODEL CommandMEANS/INTERCEPTS/THRES
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MODEL Commandall of the variables i
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MODEL Commandto be estimated with s
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MODEL Commandy13 y14 y15 (1);specif
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MODEL CommandThe statement above fi
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MODEL CommandThe list function can
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MODEL Commandy2 ON x2 (p4);y3 ON x1
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MODEL CommandLABELING CATEGORICAL L
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MODEL Commandestimated. For example
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MODEL CommandQuadraticPiecewiseLine
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MODEL CommandMultiple groupfor a bi
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MODEL CommandMultipleindicator for
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MODEL CommandFollowing is an exampl
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MODEL Commandthe outcome in a growt
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MODEL CommandTHE MODEL INDIRECT COM
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MODEL CommandMODEL INDIRECT:y3 IND
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MODEL Commandwhere c is a parameter
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MODEL CommandCONSTRAINT statement o
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MODEL CommandMODEL:y ON x1 (p1)x2 (
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MODEL CommandMODEL label:MODEL foll
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MODEL Commandare used with the %WIT
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MODEL CommandIn addition, the NCSIZ
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MODEL Commandused in computing cove
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MODEL Commandis a categorical laten
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OUTPUT, SAVEDATA, And PLOT Commands
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MONTECARLO CommandCHAPTER 19MONTECA
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MONTECARLO Commandto generate varia
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MONTECARLO Command(ordinal), unorde
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MONTECARLO Commandvariable the numb
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MONTECARLO CommandCSIZESThe CSIZES
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MONTECARLO CommandThe PATPROBS opti
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MONTECARLO Commandnumber 1. In the
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MONTECARLO CommandWith a zero-infla
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MONTECARLO Commandtime intervals in
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MONTECARLO Commandwhere z1, z2, and
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MONTECARLO CommandSAVEThe SAVE opti
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A Summary Of The Mplus LanguageCHAP
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A Summary Of The Mplus LanguageDATA
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A Summary Of The Mplus LanguageTHE
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A Summary Of The Mplus LanguageVARI
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A Summary Of The Mplus LanguageMUCO
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A Summary Of The Mplus LanguageMODE
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A Summary Of The Mplus LanguagePATT
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A Summary Of The Mplus LanguageTHE
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REFERENCESAgresti, A. (1996). An in
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Browne, M.W., Cudeck, R., Tateneni,
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Jennrich, R.I. (1974). Simplified f
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Marlow, A.J., Fisher, S.E., Francks
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Muthén, B., du Toit, S.H.C. & Spis
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Vermunt, J.K. (2003). Multilevel la
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INDEX%BETWEEN%, 624-25%class label%
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DATA, 456DATA IMPUTATION, 463-64ROW
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zero-inflated count outcome, 110-11
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missing data, 39-40, 339-41, 342-43
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NOMEANSTRUCTURE, 540-41NOMINALMonte
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sequential regression, 463-64SERIES
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751
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