Structural Equation Modelling for longitudinal data
Workshop Description
Longitudinal models are a broad class of approaches for understanding change and stability over time. There are many different approaches for understanding repeated measures data, but individual researchers are often exposed to only one framework (at best) and may be unaware of the full range of options for longitudinal modeling. In this workshop, we will navigate these issues, focusing on the multi-faceted decision process needed for model-selection among various popular modeling frameworks, specific modeling decisions that are required for appropriate analysis and inference, and steps for best connecting theories of change with model implementation. Where they arise, we will also compare the relative strengths of various modeling frameworks, including mixed effect (multilevel and generalized additive) and structural equation (latent curve and latent change score) models for fitting the various model options. The workshop will have two parts, one focused on developing a theoretical understanding of the models and their assumptions, and the second focused on gaining practical experience with fitting the models in R (with mentions of additional options available in other programs).
Schedule
Monday, March 27
9:00 – 10:30: Introduction to Longitudinal Modeling (Goals, Approaches, Data)
10:30 – 10:45: Break
10:45 – 12:00: Approaches to Incorporating Time
12:00 – 13:30: Lunch
13:30 – 14:45: Determining the Optimal Shape of Change Over Time
14:45 – 15:00: Break
15:00 – 16:30: Hack-a-thon Part 1
16:30 – 17:00: Q&A and Wrap-up
Tuesday, March 28
9:00 – 10:30: Covariates & Distal Outcomes
10:30 – 10:45: Break
10:45 – 12:00: Nested Data, Advanced Applications
12:00 – 13:30: Lunch
13:30 – 14:00: Q&A and Wrap-up
14:00 – 16:30: Hack-a-thon Part 2