Mplus Lab Sessions

Education 255C, Winter 2006

T.A.: Karen Nylund
Email: knylund@ucla.edu

This page will be updated throughout the quarter,
following the content of the course.

Week 1: Introduction to Mplus and Regression           

Introduction to Mplus

• Summary of Mplus Language  

Hopkins Data Example

(See “Introductory Example” on the syllabus web page for description of this study)

• Type=Basic Output for Hopkins Data
• Regression of Y5 on treatment
• Regression of Y5 on treatment and pretest
• Regression of Y5 on treatment and pretest, using the subset that had high pretest scores
• Regression of Y5 on treatment, pretest and interaction of treatment indicator and pretest
  

• Type = Basic Output for LSAY Data females
• Linear Growth Model
• Non-linear Growth Model
• Alternate Non-linear Growth Model
• Freed Residual Covariance between Time Adjacent Points
• Time Invariant Covariate
• Quadratic Growth
• Alternate Growth Model Language
  

Week 4:  Continuation of Growth Modeling  

• Type=Basic of LSAY data for math outcomes (graphics included)
• Simple linear growth model
• Linear growth model on with correlated outcomes
• Quadratic growth model with covariates
• Linear growth model with covariates and correlated residuals
• Piecewise growth model with covariates
  

Week 5: Investigating Treatment Effects 

• Input Summary of the Muthen & Curran (1997) Method
  • The Muthen & Curran (1977) article can be found on the Syllabus link on the course web page
• Partial Output for  M-C method, no baseline treatment interaction
• Partial Output for  M-C method, with baseline treatment interaction

These two output files show the two growth model parameterization options in Mplus using LSAY data

• Parameterization 1
• Parameterization 2
  

Week 6:  Growth Mixture Modeling (GMM) with the LSAY math achievement data 

• Simple Linear Growth Model
• 1-class GMM with default settings
• 2-class GMM with class-specific intercept variance
• 2-class GMM with class-specific intercept variance
• 2-class GMM with class-specific intercept and slope variance

Week 7:

Lecture 11: Mixtures and local solutions.

• 3-class GMM with default settings
• 3-class GMM with class-specific intercept variance
• 3-class GMM with class-specific intercept and slope variance
• 4-class GMM with class-specific intercept variance
• 4-class GMM with class-specific intercept and slope variance

Week 8:  Assessing treatment effects with GMM

**Note that the following files are very long, some over 50 pages. You may want to only print the first 10 pages or so.

• Hopkins Example 1 ***
• Hopkins Example 2 ** 
• Hopkins Example 3 **

Week 9:  Multilevel Analysis

• ex9.1 dat
• ex9.1.inp
• ex9.1.out
• ex9.2.dat
• ex9.2.inp
• ex9.2.out
• ex11.4.inp
• ex11.4.out
• anovaB.dat
• 2 level ancova between.inp
• 2 level ancova between.out
• mc 2 level ancova between.inp
• mc 2 level ancova between.out
• ancovaW.dat
• 2 level ancova within group mean centered.inp
• 2 level ancova within group mean centered.out