Ellen Hamaker is Professor of Longitudinal Data Analysis. Longitudinal data are popular in the social and behavioural sciences, because they allow us to investigate how individuals, dyads, families, or other research units change over time. 

Ellen's work focuses on developing statistical models to improve the field of panel research, which is based on data that consist of a relatively small number of repeated measures (i.e., typically T < 8), as well as the field of intensive longitudinal research, which is based on data that consist of a large number of repeated measures (i.e., say T>20). Her most important contributions to these fields are the Random Intercept Cross-Lagged Panel Model (RI-CLPM) that was proposed as an alternative for the popular Cross-Lagged Panel Model (Hamaker, Kuiper & Grasman, 2015), and Dynamic Structural Equation Modeling (DSEM), which is a flexible toolbox in the software package Mplus which she helped to develop, and that can be used to analyse intensive longitudinal data (Asparouhov, Hamaker & Muthén, 2018).

A recurrent theme in Ellen's work is the importance of separating within-person dynamics from stable between-person differences. In addition, Ellen focuses on how insights from modern causality literature can be put to use in social and behavioural sciences.  

On April 12, 2019, Ellen gave her inaugural speech, which can be found here: OratieHamaker2019.pdf (in Dutch).