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

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Cluster information for SSCHOOLCluster information for PSCHOOLSize (s) Cluster ID with Size s Size (s) Cluster ID with Size s20 9 10 5021 20 12 4322 12 13 41 2423 24 15 47 23 5 2224 15 16 30 926 3 17 17 7 26 3827 1 30 18 1 3 6 45 14 2828 23 19 29 17 49 35 21 2030 5 20 16 231 26 25 21 40 32 46 11 19 13 4 3932 2 22 34 2733 8 13 23 15 1834 4 18 24 25 44 3735 29 25 36 31 1037 27 11 26 839 22 19 27 4241 16 29 48 1242 21 7 31 3345 1446 1047 2848 6 Bengt Muthén & Tihomir Asparouhov New Developments in Mplus Version 7 150/ 159
WITHIN levelVariancesPosterior One-Tailed 95% C.I.Estimate S.D. P-Value Lower 2.% Upper 2.5% Significanceachieve 0.513 0.024 0.000 0.470 0.564 *BETWEEN sschool levelVariancesachieve 0.075 0.028 0.000 0.040 0.147 *BETWEEN pschool levelMeansachieve 6.341 0.084 0.000 6.180 6.510 *Variancesachieve 0.183 0.046 0.000 0.116 0.294 *Bengt Muthén & Tihomir Asparouhov New Developments in Mplus Version 7 151/ 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
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Critique of 1-Step: Vermunt (2010)H
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Advantage of 1-Step Over 3-StepLow-
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Auxiliary Variables In Mixture Mode
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3-Step Mixture Modeling For Special
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3-Step Mixture Modeling For Special
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Latent Transition AnalysisNew devel
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LTA Example 1: ECLS-K, ContinuedThr
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LTA Example 2: Mover-Stayer LTA Mod
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Latent Transition Probabilities Inf
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LTA with a Binary Covariate as Know
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%c1#3%LTA with a Binary [u11$1] Cov
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LTA Example 1: ECLS-K, Mplus InputT
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LTA Example 1: ECLS-K, Mplus Input,
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LTA Example 1: ECLS-K, Mplus Input,
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TECHNICAL 15 Output, ContinuedP(C2=
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Interaction Displayed Two Equivalen
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LTA with a Continuous Covariate: UG
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[u11$1] (11);LTA with a Continuous
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LTA Exmple 1: ECLS-K,Adding Poverty
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LTA Calculator Applied to Poverty,
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LTA Example 1: ECLS-K, Mplus Input
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Mover-Stayer LTA Modeling In Logist
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! Relating c2 and c3 to c: (Stayers
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Measurement Part of the ModelMODEL
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Mover-Stayer LTA in Probability Par
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Mover-Stayer LTAand Predicting Move
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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
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: TITLE:DATA:VARIABLE:ANALYSIS:MODEL:
Page 153 and 154: WITHIN levelachieve ONPosterior One
Page 159: Cross-Classified Regression: Input
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New Developments in Mplus Version 7: Part 2 - Muthén & Muthén

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