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.
- Human Development and Family Studies - HDFS
- Adulthood and Aging
- Graduate Program
- M.S., 2004, Psychology, University of Debrecen (Hungary)
- Ph.D., 2009, Quantitative Psychology, University of Leuven (Belgium)
University Park, PA 16802
Institute for CyberScience
- 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
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.