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

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Auxiliary Variables In Mixture Modeling: Latent ClassPredictor SimulationThe 3-step procedure again outperforms the PC proceduresubstantially in terms of bias, mean squared error and confidenceinterval coverageThe efficiency of the 3-step procedure is comparable to the1-step method when the entropy is 0.6 or higherBengt Muthén & Tihomir Asparouhov New Developments in Mplus Version 7 46/ 159
Auxiliary Variables In Mixture Modeling: Other ApproachesClark & Muthén (2009). Relating latent class analysis results tovariables not included in the analysisExtensive simulation of other methods, such as Most LikelyClass Membership Regression, Probability Regression,Probability-Weighted Regression, even Bayes Imputations, showthat all these methods have biased estimatesBengt Muthén & Tihomir Asparouhov New Developments in Mplus Version 7 47/ 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 45: Auxiliary Variables In Mixture Mode
Page 51 and 52: 3-Step Mixture Modeling For Special
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,
Page 125 and 126:
3-Level MIMIC Analysis, Continuedy1
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3-Level MIMIC Analysis, Monte Carlo
Page 129 and 130:
Page 131 and 132:
Page 133 and 134:
3-Level Growth AnalysisBengt Muthé
Page 135 and 136:
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
Page 149 and 150:
TITLE:DATA:VARIABLE:ANALYSIS:MODEL:
Page 151 and 152:
WITHIN levelVariancesPosterior One-
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WITHIN levelachieve ONPosterior One
Page 155 and 156:
Page 157 and 158:
Page 159:
Cross-Classified Regression: Input
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New Developments in Mplus Version 7: Part 2 - Muthén & Muthén

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