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The table below provides an outline of the topics covered in the LDA workshop. LDA encompasses random effect models for change, latent growth curve, latent change score models, and survival analysis and other models for event occurrence. Briefly, day 1 introduces the kinds of research questions approached using LDA; an introduction to Mplus software; using Mplus software; and the importance of workflow; finally, LDA models are introduced. By the end of day 1 we have demonstrated that the latent growth curve model can be specified in a way that produces the same parameter estimates as mixed effect models for change (e.g., SAS/PROC MIXED, Stata/xtmixed). Day 2 expands on latent growth curve models, discusses data handling issues, model fit assessment, and latent classes for growth. Day 3 covers applications and more advanced topics.
Day 1
Day 1
Introduction and Overview Longitudinal Data Analysis
Workshop Objectives
Content Covered in Workshop
Resources to Continue Learning After Workshop
Introduction to Longitudinal Data Analysis (LDA)
Alternatives and Challenges
Problems with Change Scores
Modeling as a Research Paradigm
Latent Growth Curve Modeling (LGM)
Thinking about and modeling time
Orientation to General Latent Variable Modeling Framework
Path Diagram Notation
Covariance Structure Modeling Perspectives on Latent Variables
Orientation to Mplus Modeling
How to write an Mplus command file
Example Data Set Overview
Getting Data into Mplus (Using SAS, SPSS, Stata, R; PC and MAC)
Workflow and Reproducibility
Confirmatory Factor Analysis (CFA)
Growth Curve Modeling
Compare with SAS/PROC Mixed
Day 2
Day 2
Latent Growth Curve Models
LGM Model Specification
LGM with covariates
Time Invariant Covariates
Time Varying Covariate
Using Mplus for LGM
Mplus Command Syntax
Missing Data Handling
Model Fit Assessment
Data Handling: Centering Covariates
Non-Linear Development
Growth Mixture Modeling (GMM)
Latent Class Analysis (LCA) for Growth
Assessing Fit
Determining the number of classes
Exploratory and Confirmatory LCA
GMM with Covariates
Importance of Theory and Hypotheses for Driving Data Analysis
Multilevel Model Approach to Growth Curve Modeling with Mplus
Multiple Indicator Growth Curve Model
Modeling Retest Effects and Other Methods Artifacts
Day 3
Day 3
Applications
Knownclass Mixture Model
LGM in Randomized Controlled Trials
Acceleration/Deceleration
Punctuation
Advanced and alternative models
Latent Change Score Models
Dual Change Score Model
Strategies for Building Complex Models
Accelerated Longitudinal Designs
Multilevel LGM
Parallel Process LGM
Multiple Indicator LGM with Measurement Non-Invariance
Joint Survival and Growth Models
Resources to Continue Learning After Workshop
Google Sites
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