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Zita Oravecz's research work has focused on developing and disseminating innovative computational statistical techniques to address questions related to emotional and cognitive functioning development. She is affiliated with the Department of Human Development and Family Studies, where she employs intensive longitudinal data analysis, cognitive process modeling, and multilevel Bayesian modeling. Her work emphasizes affective science and ecological momentary assessment in intervention settings. Notable research projects include MindTrack, which involves multi-timescale process models to disentangle subtle cognitive decline and learning effects, and Wear-IT, aimed at using mobile technology to develop individualized interventions to prevent relapse in addiction. She has a strong interest in studying individual differences from a process modeling perspective, striving to apply state-of-the-art statistical approaches to areas of substantive research, particularly emotion and cognition, that are challenging to study without innovative methods of analysis.
GRE scores are highly recommended but not strictly required for Applied Linguistics.