New Developments in Mplus Version 7: Part 2 - Muthén & Muthén

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Auxiliary Variables In Mixture ModelingThe latent class variable can be identified by any mixture model,not just LCA, for example Growth Mixture ModelsMultiple auxiliary variables can be analyzed at the same timeAuxiliary variables can be included in a Montecarlo setupThe 3-step procedure can be setup manually for other types ofmodels, different from the distal outcome model and the latentclass regression. For example, distal outcomes regressed on thelatent class variable and another predictorBengt Muthén & Tihomir Asparouhov New Developments in Mplus Version 7 50/ 159
3-Step Mixture Modeling For Special ModelsHow can we estimate a mixture regression model independentlyof the LCA model that defines CY = α c + β c X + εWe simulate data with α 1 = 0, α 2 = 1, β 1 = 0.5, β 2 = −0.5Step 1: Estimate the LCA model (without the auxiliary model)with the following optionSAVEDATA: FILE=1.dat; SAVE=CPROB;The above option creates the most likely class variable NStep 2: Compute the error rate for N. In the LCA output findBengt Muthén & Tihomir Asparouhov New Developments in Mplus Version 7 51/ 159
Page 1 and 2: New Developments in Mplus Version 7
Page 3 and 4: Table of Contents II6 3-Level Analy
Page 5 and 6: Random Loadings In IRTY ijk - outco
Page 7 and 8: Random Loadings In IRT ContinuedMod
Page 9 and 10: PISA Results - Discrimination (Mean
Page 11 and 12: Country Specific Mean Ability Param
Page 13 and 14: Random Loadings In IRT ContinuedRan
Page 15 and 16: Testing For Non-Zero Variance Of Ra
Page 17 and 18: Testing For Non-Zero Variance Of Ra
Page 19 and 20: Results: Tech16Bengt Muthén & Tiho
Page 25 and 26: Random Loadings In IRT Continued:Ad
Page 27 and 28: Random Loadings In IRT Continued:Ad
Page 29 and 30: Two-Level Random Loadings:Individua
Page 31 and 32: Individual Differences Factor Analy
Page 37 and 38: Critique of 1-Step: Vermunt (2010)H
Page 39 and 40: Advantage of 1-Step Over 3-StepLow-
Page 41 and 42: Auxiliary Variables In Mixture Mode
Page 49: Auxiliary Variables In Mixture Mode
Page 53 and 54: 3-Step Mixture Modeling For Special
Page 55 and 56: Latent Transition AnalysisNew devel
Page 57 and 58: LTA Example 1: ECLS-K, ContinuedThr
Page 59 and 60: LTA Example 2: Mover-Stayer LTA Mod
Page 61 and 62: Latent Transition Probabilities Inf
Page 63 and 64: LTA with a Binary Covariate as Know
Page 65 and 66: %c1#3%LTA with a Binary [u11$1] Cov
Page 67 and 68: LTA Example 1: ECLS-K, Mplus InputT
Page 69 and 70: LTA Example 1: ECLS-K, Mplus Input,
Page 71 and 72: LTA Example 1: ECLS-K, Mplus Input,
Page 73 and 74: TECHNICAL 15 Output, ContinuedP(C2=
Page 75 and 76: Interaction Displayed Two Equivalen
Page 77 and 78: LTA with a Continuous Covariate: UG
Page 79 and 80: [u11$1] (11);LTA with a Continuous
Page 81 and 82: LTA Exmple 1: ECLS-K,Adding Poverty
Page 83 and 84: LTA Calculator Applied to Poverty,
Page 85 and 86: LTA Example 1: ECLS-K, Mplus Input
Page 87 and 88: Mover-Stayer LTA Modeling In Logist
Page 89 and 90: ! Relating c2 and c3 to c: (Stayers
Page 91 and 92: Measurement Part of the ModelMODEL
Page 93 and 94: Mover-Stayer LTA in Probability Par
Page 95 and 96: Mover-Stayer LTAand Predicting Move
Page 97 and 98: Exploratory LCA Using Bayesian Anal
Page 99 and 100: Exploratory LCA Using Bayes Analysi
Page 101 and 102:
Qu-Tan-Kutner ExampleQu T, Tan M, K
Page 103 and 104:
Qu-Kutner Example: Bayes Explorator
Page 105 and 106:
Types Of Observed Variables In 3-Le
Page 107 and 108:
3-Level RegressionLevel 1 : y ijk =
Page 109 and 110:
3-Level Regression Example: UG Exam
Page 111 and 112:
3-Level Regression Example: Nurses
Page 113 and 114:
Input for Nurses Data, ContinuedMOD
Page 115 and 116:
Model Results for Nurses Data, Cont
Page 117 and 118:
3-Level Path Analysis: UG Example 9
Page 119 and 120:
3-Level Path Analysis: Monte Carlo
Page 121 and 122:
3-Level Path Analysis: Monte Carlo
Page 123 and 124:
Monte Carlo Output for UG Ex 9.21,
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3-Level MIMIC Analysis, Continuedy1
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3-Level MIMIC Analysis, Monte Carlo
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Page 131 and 132:
Page 133 and 134:
3-Level Growth AnalysisBengt Muthé
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3-Level Growth Analysis InputTITLE:
Page 137 and 138:
TYPE=THREELEVEL COMPLEX, ContinuedO
Page 139 and 140:
Example Of Multiple Imputation For
Page 141 and 142:
Cross-Classified Data StructureStud
Page 143 and 144:
Cross-Classified ModelingWhy do we
Page 145 and 146:
Cross-Classified Regression: UG Exa
Page 147 and 148:
An Example of Cross-Classified Mode
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TITLE:DATA:VARIABLE:ANALYSIS:MODEL:
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WITHIN levelVariancesPosterior One-
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WITHIN levelachieve ONPosterior One
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Page 157 and 158:
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Cross-Classified Regression: Input
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New Developments in Mplus Version 7: Part 2 - Muthén & Muthén

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