Intensive longitudinal studies (e.g., experience sampling studies) have demonstrated that detecting changes in statistical features across time is crucial to better capture and understand psychological phenomena. For example, it has been uncovered that emotional episodes are characterized by changes in both means and correlations. In psychopathology research, recent evidence revealed that changes in means, variance, autocorrelation and correlation of experience sampling data can serve as early warning signs of an upcoming relapse into depression. In this talk, I will discuss flexible statistical tools for retrospectively and prospectively capturing such changes. First, I will present the KCP-RS framework, a retrospective change point detection framework that can be tailored to capture changes in not only the means but in any statistic that is relevant to the researcher. Second, I will turn to the prospective change detection problem, where I will argue that statistical process control procedures, originally developed for monitoring industrial processes, are promising tools but need tweaking to the problem at hand.
about the speaker
Eva Ceulemans is a Full Professor of Quantitative Psychology at the University of Leuven (Belgium). She received her PhD in 2003 (under the supervision of Iven Van Mechelen). Her current research interests include time series analysis, sample size planning for intensive longitudinal studies, retrospective and prospective change point detection, and dynamical modeling of intensive longitudinal dyadic data. A crucial aspect of her research is building bridges between data analysis methods and substantive psychological research questions. Eva Ceulemans’ work has been published in Psychometrika, Psychological Methods, Advances in Methods and Practices of Psychological Science, Behavior Research Methods, and Multivariate Behavioral Research. Eva Ceulemans is an Associate Editor of the British Journal of Mathematical and Statistical Psychology.