Longitudinal Panel Models with Structural Equations

About

Panel data are gathered in designs that measure the same set of participants on multiple occasions, typically at regular intervals. Structural equation models (SEM) for panel data include a variety of autoregressive models (e.g., simplex models), as well as cross-lagged panel models that estimate reciprocal relations between two or more repeatedly measured variables. Day 1 of this course will introduce you to the fundamentals of autoregressive and cross-lagged panel models in SEM for both observed and latent variables. On Day 2, you will about more sophisticated extensions, such as the random-intercept cross-lagged panel model and how to incorporate time-invariant predictors in your models for change.

Course leader

Terrence D. Jorgensen, PhD, University of Amsterdam

Target group

Professionals, Researchers, and Students, who wish to develop high-quality surveys and employ up-to-date statistical methods for survey data analysis. Potential attendees include survey practitioners, marketing professionals, social science students, and researchers.