Advanced structural equation modeling

Longitudinal data is often of utmost importance to developmental scientists. Analyzing longitudinal data not only allows us to better understand how young people develop, but also to better comprehend within-person processes controlling for between-person differences. In this way, longitudinal data can help distinguish developmental processes from individual differences. Furthermore, longitudinal data can inform us about individual differences in developmental processes. Therefore, applying techniques to analyze longitudinal data is a necessary skill for researchers studying development and developmental processes. 

In this workshop, you will acquire hands-on knowledge on conducting advanced SEM analyses to study developmental order and processes controlling for individual differences (Random-Intercept Cross Lagged Panel Models), growth (Latent Growth Curve Models), and individual differences in developmental processes (Latent Class Growth Analysis/Growth Mixture Models). We will use both Mplus and R. In light of recent critical discourses, we will discuss between-person and within-person models, and how to choose the right analyses for your research questions. We will further provide a glimpse into the modelling of intense longitudinal data (e.g., DSEM). 

This workshop is aimed at researchers who want to extend their knowledge about SEM modelling techniques, and learn how to use these models in their own analyses. Participants should be familiar with basic SEM models (e.g., path analysis, growth models) and should have experience with running analyses in Mplus and/or R. To follow the course and practical exercises, at least one of these programs should be installed on your computer.

Workshop organizer
STEFANOS MASTROTHEODOROS is a postdoctoral researcher at the department of Youth & Family at Utrecht University, the Netherlands. He has completed a PhD at the University of Athens, Greece, where he investigated the developmental interplay between personal identity, close relationships, and developmental tasks during adolescence. His second PhD, completed at Utrecht University, revolved around the development of parenting, parent-adolescent relationships, and family conflict, during adolescence. He is a Data Analyst with R, and has experience in applying longitudinal models to answer developmental questions.

SUSANNE SCHULZ is a PhD candidate at the Youth & Family department at Utrecht University. In her PhD project, she uses longitudinal data to examine mechanisms of the intergenerational transmission of psychopathology. Susanne completed the Research Master Child Development and Education (cum laude) at the University of Amsterdam and is part of the CONSORTIUM INDIVIDUAL DEVELOPMENT. She is excited about novel statistical analyses, which she implements in her own work to answer complex questions about child and adolescent development.