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

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3-Level MIMIC Analysis, Monte Carlo Input:5 Students (14 Parameters) in 30 Classrooms (13 Parameters)in 50 Schools (28 Parameters)MONTECARLO:ANALYSIS:NAMES = y1-y6 x1 x2 w z;NOBSERVATIONS = 7500;NREPS = 500;CSIZES = 50[30(5)];NCSIZE = 1[1];!SAVE = ex9.22.dat;WITHIN = x1 x2;BETWEEN = (level2) w (level3) z;TYPE = THREELEVEL RANDOM;ESTIMATOR = MLR;Bengt Muthén & Tihomir Asparouhov New Developments in Mplus Version 7 128/ 159
3-Level MIMIC Analysis, Monte Carlo OutputREPLICATION 499:THE STANDARD ERRORS OF THE MODEL PARAMETER ESTIMATES MAY NOT BETRUSTWORTHY FOR SOME PARAMETERS DUE TO A NON-POSITIVE DEFINITEFIRST-ORDER DERIVATIVE PRODUCT MATRIX. THIS MAY BE DUE TO THE STARTINGVALUES BUT MAY ALSO BE AN INDICATION OF MODEL NONIDENTIFICATION. THECONDITION NUMBER IS -0.239D-16. PROBLEM INVOLVING PARAMETER 51.THE NONIDENTIFICATION IS MOST LIKELY DUE TO HAVING MORE PARAMETERS THAN THENUMBER OF LEVEL 3 CLUSTERS. REDUCE THE NUMBER OF PARAMETERS.REPLICATION 500:THE STANDARD ERRORS OF THE MODEL PARAMETER ESTIMATES MAY NOT BETRUSTWORTHY FOR SOME PARAMETERS DUE TO A NON-POSITIVE DEFINITEFIRST-ORDER DERIVATIVE PRODUCT MATRIX. THIS MAY BE DUE TO THE STARTINGVALUES BUT MAY ALSO BE AN INDICATION OF MODEL NONIDENTIFICATION. THECONDITION NUMBER IS -0.190D-16. PROBLEM INVOLVING PARAMETER 52.THE NONIDENTIFICATION IS MOST LIKELY DUE TO HAVING MORE PARAMETERS THAN THENUMBER OF LEVEL 3 CLUSTERS. REDUCE THE NUMBER OF PARAMETERS.Bengt Muthén & Tihomir Asparouhov New Developments in Mplus Version 7 129/ 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
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
Page 127: 3-Level MIMIC Analysis, Monte Carlo
Page 131 and 132: 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 and 150: TITLE:DATA:VARIABLE:ANALYSIS:MODEL:
Page 151 and 152: WITHIN levelVariancesPosterior One-
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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