Zita Oravecz's research work has been focused on developing and disseminating innovative computational and statistical techniques for addressing questions related to emotional and cognitive functioning and development.
University Park, PA 16802
Ph.D., 2009, Quantitative Psychology, University of Leuven (Belgium)
- intensive longitudinal data analysis
- cognitive process modeling
- multilevel Bayesian modeling
- affective science
- ecological momentary assessment and intervention
Zita Oravecz has been working towards building a framework that provides for delivery and assessment of real-time, context-aware, person-centered interventions for improving health and maximizing human potential. She has been developing hierarchical Bayesian process models, which offer a framework for testing theories by disentangling and quantifying latent processes that become confounded in observed data. By design, parameters of a process model correspond to theory-backed concepts such as dynamical regulatory mechanisms (see, e.g., Oravecz, Tuerlinckx, & Vandekerckhove, 2016) or cognitive ability levels (see, e.g., Oravecz, Anders & Batchelder, 2015), and allow us to make statements about these concepts directly. She casts these models into the hierarchical/multilevel framework to study the data generating processes in the context of both population- and individual-level questions. She implements models in the Bayesian statistical framework, and been devoted to the dissemination of the advantages of Bayesian methods, for example via developing user-friendly software tools (see, e.g., Oravecz & Muth, 2018).
Wear-IT: using mobile technology to develop individualized interventions to prevent relapse in addiction; developing longitudinal and cognitive process models
Institute for CyberScience
Research interest in studying individual differences from a process modeling perspective. The general goal is to develop and apply state-of-the-art statistical approaches to particular areas of substantive research (in my case emotion and cognition) that would be difficult or impossible to study without novel methods of analysis.