Typical for developmental psychology are models that capture change over time, such as latent growth (mixture) models and to a lesser extent cross-lagged panel models too. Such models have typically been applied aiming to capture change over time in individuals.

In a latent growth model, the time or measurement occasion variable is defined in the measurement model of the latent factors. To be more specific consecutive measurements are modeled by a latent variable for the intercept of the growth curve, and a second latent variable for the slope of the curve . With latent growth analysis, I refer to person-centred techniques to estimate individuals developing over time. Latent growth modelling assumes that all individuals are drawn from one population. To be more specific, consecutive measurements are summarized by a growth trajectory modeled by latent variables, typically denoting the intercept of the growth curve, the linear slope of the curve and the deviation of linearity.

The development over time can be combined with a mixture component to estimate trajectory membership. Mixture modelling means that growth parameters (i.e., intercept, slope, etc.) vary across a number of pre-specified, unobserved subpopulations. These subpopulations are established through scores on one or more categorical latent variables. These additional variables allow for calculating growth trajectories per group. Hence, the usage of such variables results in separate latent growth models for each (unobserved) group, each with its unique set of growth parameters.

In this project I have published on applying longitudinal models to empirical data and how to properly report on LGMM models. Furthermore, I study the Bayesian counterpart of longitudinal models, and then particularly how the prior information can best be defined for small samples . Several of such papers are currently under review. Once these have been accepted, more information on them will be made available.


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PhD Student

Sannes PhD focuses on the use of informative priors in latent growth models with small sample sizes. She is interested in how prior knowledge can be used to compensate for small sample sizes.

Former team member

The analyses of two of the chapters of her PhD thesis are performed with Bayesian statistics in MPlus. Rens is copromotor (defence is March 3rd), thesis titled A coach in your pocket. On chronic cancer-related fatigue and...

Former team member

In June 2012 I started my PhD project at the Helen Dowling Institute, which is a mental healthcare facility for cancer patients. There, I quantitatively investigated the effectiveness of two eHealth interventions for...

PhD Student

Marthes project is focused on psychological consequences of pediatric burn injury. She primarily examines child and parent posttraumatic stress reactions with the use of prospective data and advanced statistical modelling (such as multilevel and structural equation models).

PhD Student

Inges research concerns the development of students with special educational needs due to psychiatric and/or behavior problems. Students with emotional and/or behavior problems (EBD) perform worse than normally developing students with respect to their academic and their ...

Sarah Depaoli
Assistant Professor at the University of California, Merced
Sarah’s research interests are largely focused on issues surrounding Bayesian estimation of latent variable models. She has a particular interest in estimation issues arising from nonlinear growth patterns over time. She is also interested in improving accuracy of uncovering unobserved (latent) groups of individuals. She is currently working with several students that are involved in research spanning a wide range of methodological topics .
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Nancy van Loey
Marit Sijbrandij
Jeroen Vermunt