Zita Oravecz, PhD

Assistant Professor of Human Development & Family Studies

I aim to advance the use of models that capture individual differences in terms of person-specific process model parameters, and apply these models to large-scale (e.g., intensive longitudinal) data. These process models offer a framework for testing substantive theories by proposing concrete mechanisms that underlie observed data. Currently I am focusing on developing dynamic models of well-being.  In my lab we are conducting ecological momentary assessment studies to collect intensive longitudinal data on people’s emotional experiences and cognitive evaluations of their daily life. By combining cognitive psychometric techniques and dynamical systems modeling, my goal is to unpack the mechanisms, pathways, and synchronicity dynamics of emotional and cognitive elements of well-being. Moreover, I am interested in studying how these well-being elements synchronize with physiological measures, such as blood volume pulse and electrodermal activity. The goal is to explore how person-specific patterns in these physiological variables can improve understanding of changes in self-reported well-being measures, and provide for deployment of real-time interventions.The complexity of these models requires flexible methods for parameter estimation. Bayesian statistics offers these methods, while also providing a principled framework for statistical inference. Therefore an interwoven theme in my research is promoting Bayesian methods.


Helm, J. Lee, Castro-Schilo, L., & Oravecz, Z.. (2017). Bayesian Versus Maximum Likelihood Estimation of Multitrait–Multimethod Confirmatory Factor Models. Structural Equation Modeling: A Multidisciplinary Journal, 1 - 14. presented at the May-10-2017. doi:10.1080/10705511.2016.1236261
Wood, J., Oravecz, Z., Vogel, N., Benson, L., Chow, S. - M., Cole, P., et al.. (2017). Modeling Intraindividual Dynamics Using Stochastic Differential Equations: Age Differences in Affect Regulation. The Journals of Gerontology: Series B. presented at the Feb-03-2017. doi:10.1093/geronb/gbx013
Oravecz, Z., Huentelman, M., Vandekerckhove, J., & Jones, M.. (2016). Sequential Bayesian updating for Big Data. In Big Data in Cognitive Science: From Methods to Insights (pp. 13-33). Sussex, UK: Psychology Press (Taylor & Francis).
Oravecz, Z., Muth, C., & Vandekerckhove, J.. (2016). Do People Agree on What Makes One Feel Loved? A Cognitive Psychometric Approach to the Consensus on Felt Love. (X. Weng, Ed.)PLOS ONE, 11(4), e0152803. presented at the Jan-04-2016. doi:10.1371/journal.pone.0152803
Oravecz, Z., Tuerlinckx, F., & Vandekerckhove, J.. (2016). Bayesian Data Analysis with the Bivariate Hierarchical Ornstein-Uhlenbeck Process Model. Multivariate Behavioral Research, 51(1), 106 - 119. presented at the Feb-01-2016. doi:10.1080/00273171.2015.1110512
Loken, E., Oravecz, Z., Tucker, C., & Linder, F.. (2015). Psychometric Analysis of Residence and MOOC Assessments. In 2015 ASEE Annual Conference and Exposition2015 ASEE Annual Conference and Exposition Proceedings. Seattle, Washington: ASEE Conferences. doi:10.18260/p.24621
Oravecz, Z., Anders, R., & Batchelder, W. H.. (2015). Hierarchical Bayesian Modeling for Test Theory Without an Answer Key. Psychometrika, 80(2), 341 - 364. presented at the Jan-06-2015. doi:10.1007/s11336-013-9379-4
Oravecz, Z., FAUST, K., BATCHELDER, W. H., & LEVITIS, D.. (2015). Studying the Existence and Attributes of Consensus on Psychological Concepts by a Cognitive Psychometric Model. The American Journal of Psychology, 128, 61. presented at the Jan-01-2015. doi:10.5406/amerjpsyc.128.1.0061
Ebner-Priemer, U. W., Houben, M., Santangelo, P., Kleindienst, N., Tuerlinckx, F., Oravecz, Z., et al.. (2015). Unraveling affective dysregulation in borderline personality disorder: A theoretical model and empirical evidence. Journal of Abnormal Psychology, 124(1), 186 - 198. presented at the Jan-01-2015. doi:10.1037/abn0000021
Oravecz, Z., Faust, K., & Batchelder, W. H.. (2014). An Extended Cultural Consensus Theory Model to Account for Cognitive Processes in Decision Making in Social Surveys. Sociological Methodology, 444, 185 - 228. presented at the Jan-08-2014. doi:10.1177/0081175014529767
Anders, R., Oravecz, Z., & Batchelder, W. H.. (2014). Cultural consensus theory for continuous responses: A latent appraisal model for information pooling. Journal of Mathematical Psychology, 61, 1 - 13. presented at the Jan-08-2014. doi:10.1016/j.jmp.2014.06.001
Oravecz, Z., Vandekerckhove, J., & BATCHELDER, W. H.. (2014). Bayesian Cultural Consensus Theory. Field Methods, 26(3), 207 - 222. presented at the Jan-08-2014. doi:10.1177/1525822X13520280
Oravecz, Z., Tuerlinckx, F., & Vandekerckhove, J.. (2011). A hierarchical latent stochastic differential equation model for affective dynamics. Psychological Methods, 16(4), 468 - 490. presented at the Jan-01-2011. doi:10.1037/a0024375
Oravecz, Z., & Tuerlinckx, F.. (2011). The linear mixed model and the hierarchical Ornstein-Uhlenbeck model: Some equivalences and differences. British Journal of Mathematical and Statistical Psychology, 64(1), 134 - 160. presented at the Jan-02-2011. doi:10.1348/000711010X498621
Kuppens, P., Oravecz, Z., & Tuerlinckx, F.. (2010). Feelings change: Accounting for individual differences in the temporal dynamics of affect. Journal of Personality and Social Psychology, 99(6), 1042 - 1060. presented at the Jan-01-2010. doi:10.1037/a0020962
Oravecz, Z., Tuerlinckx, F., & Vandekerckhove, J.. (2009). A Hierarchical Ornstein–Uhlenbeck Model for Continuous Repeated Measurement Data. Psychometrika, 74(3), 395 - 418. presented at the Jan-09-2009. doi:10.1007/s11336-008-9106-8