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

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Latent Transition Analysis: Probability ParameterizationLTA models that do not have continuous x’s can be more convenientlyspecified using PARAMETERIZATION=PROBABILITY. Considerthe probability parameterization for CLASSES = c1(3) c2(3):c1c21 2 31 p11 p12 02 p21 p22 03 p31 p32 0where the probabilities in each row add to 1 and the last c2 class arenot mentioned. The p parameters are referred to using ON. The latentclass variable c1 which is the predictor has probability parameters[c1#1 c1#2], whereas ”intercept” parameters are not included for c2.A transition probability can be conveniently fixed at 1 or 0 by usingthe p parameters.Bengt Muthén & Tihomir Asparouhov New Developments in Mplus Version 7 84/ 159
LTA Example 1: ECLS-K, Mplus Input Using ProbabilityParameterizationCLASSES = c1(3) c2(3);ANALYSIS:MODEL:TYPE = MIXTURE;STARTS = 400 80;PROCESSORS = 8;PARAMETERIZATION=PROBABILITY;%OVERALL%c2 ON c1; ! frees 6 transition probabilities! fix lower triangular transition probabilities = 0:c2#1 ON c1#2-c1#3@0;c2#2 ON c1#3@0;Bengt Muthén & Tihomir Asparouhov New Developments in Mplus Version 7 85/ 159
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New Developments in Mplus Version 7
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Table of Contents II6 3-Level Analy
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Random Loadings In IRTY ijk - outco
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Random Loadings In IRT ContinuedMod
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PISA Results - Discrimination (Mean
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Country Specific Mean Ability Param
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Random Loadings In IRT ContinuedRan
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Testing For Non-Zero Variance Of Ra
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Testing For Non-Zero Variance Of Ra
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Results: Tech16Bengt Muthén & Tiho
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Random Loadings In IRT Continued:Ad
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Random Loadings In IRT Continued:Ad
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Two-Level Random Loadings:Individua
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Individual Differences Factor Analy
Page 33 and 34: Individual Differences Factor Analy
Page 35 and 36: 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 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: LTA Calculator Applied to Poverty,
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
Page 127 and 128: 3-Level MIMIC Analysis, Monte Carlo
Page 133 and 134: 3-Level Growth AnalysisBengt Muthé
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3-Level Growth Analysis InputTITLE:
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TYPE=THREELEVEL COMPLEX, ContinuedO
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Example Of Multiple Imputation For
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Cross-Classified Data StructureStud
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Cross-Classified ModelingWhy do we
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Cross-Classified Regression: UG Exa
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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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Cross-Classified Regression: Input
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New Developments in Mplus Version 7: Part 2 - Muthén & Muthén

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