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

More documents

Recommendations

Info

Cross-Classified DataY pijk is the p−th observation for person i belonging to level 2cluster j and level 3 cluster k.Level 2 clusters are not nested within level 3 clustersExamples:Natural Nesting: Students performance scores are nested withinstudents and teachers. Students are nested within schools andneighborhoods.Design Nesting: Studies where observations are nested withinpersons and treatments/situations.Complex Sampling: Observations are nested within samplingunits and another variable unrelated to the sampling.Generalizability theory: Items are considered a random samplefrom a population of items.Bengt Muthén & Tihomir Asparouhov New Developments in Mplus Version 7 142/ 159
Cross-Classified ModelingWhy do we need to model both sets of clustering?Discover the true predictor/explanatory effect stemming from theclustersIgnoring clustering leads to incorrect standard errorsModeling with fixed effects leads to too many parameters andless accurate modelBengt Muthén & Tihomir Asparouhov New Developments in Mplus Version 7 143/ 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 21 and 22:
Page 23 and 24:
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 33 and 34:
Page 35 and 36:
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 43 and 44:
Page 45 and 46:
Page 47 and 48:
Page 49 and 50:
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
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: Cross-Classified Data StructureStud
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
show all

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

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?