Rhian Daniel, Cardiff University

“Regression models in Causal inference” (Abstract)

Rhian Daniel obtained her PhD in missing data methods at the London School of Hygiene and Tropical Medicine, and has since been working on various methodological and applied projects in the field of causal inference, moving to Cardiff University in 2017. She has worked on longitudinal causal inference in the presence of time-dependent confounding, mediation analysis with multiple mediators, and most recently a novel framework for parametric regression models, known as regression by composition.

Kim De Roover, KU Leuven

“Finding clusterwise measurement invariance with mixture multigroup factor analysis” (Abstract)

Kim De Roover obtained a PhD in methodology of educational sciences at KU Leuven. After a few postdoc years, she was an assistant professor at Tilburg University for six years. Currently, she is an associate professor in the group of Quantitative Psychology and Individual Differences at KU Leuven. Her research interests include factor analysis, factor rotation, measurement invariance, structural equation modeling, mixture modeling and model selection. She has done a lot of work on multigroup factor analysis and multigroup structural equation modeling for many groups, combined with mixture modeling (e.g., mixture multigroup factor analysis for finding clusterwise measurement invariance). She received the Classification Society Distinguished Dissertation Award for her PhD and has obtained funding for several research projects, most recently a Vidi grant from the Netherlands Organization for Scientific Research and a Starting Grant from the European Research Council.

Rebecca Kuiper, Utrecht University

“Theory-based hypothesis evaluation using information criteria for one and multiple studies” (Abstract)

My name is Rebecca Kuiper and I am an associate professor at the department of Methodology and Statistics at Utrecht University, the Netherlands. I am passionate about conducting research in the field of (bio)statistics & psychometrics and behavioural & social sciences, since there are a lot of statistical challenges I like to tackle (which in the end also contribute to society). My specializations are: 1) evaluation of theory-based/informative hypotheses, 2) (Bayesian) evidence synthesis, 3) lagged-effects modeling. I developed the AIC-criterion GORIC, which can evaluate theory-based / informative hypotheses (which often contain order-restrictions on parameters; e.g., b1 > b2 > b3). Together with a PhD-student, I developed the GORICA, an approximation of the GORIC which can easily be applied to all types of statistical models. Because of the calls for replication and the unexploited wealth of information in existing and future conceptual replications (more specifically, studies using diverse designs), I developed Bayesian evidence aggregation. Together with my team, I am now working on evidence aggregation methods using the GORIC and GORICA.

Rumen Manolov, University of Barcelona

“Single-case experimental designs (and) data analysis: One size does not fit all” (Abstract)

I am an associate professor at the Faculty of Psychology at the University of Barcelona, where I have teaching courses (in the Degree of Psychology, in the Master in Research in Behavior and Cognition, and in the Master in Cognitive Sciences and Language) related to the scientific method, descriptive and inferential statistics, and modeling since 2007. My main research interest is in single-case experimental designs (see here), focusing on methodological and data analytical aspects (incl. discussing, proposing, and testing alternatives for data analysis). I am also interested in creating and disseminating freely available user-friendly software for single-case experimental designs data analysis: a list of available alternatives, creating by several experts in the field, can be found here. I have had the privilege to work with Prof. Robyn Tate, Prof. Patrick Onghena, Prof. Jonathan J. Evans, Prof. Antonio Solanas, Dr. Mariola Moeyaert, and Dr. René Tanious, among other colleagues, interested in single-case experimental designs.

Laura Bringmann, University of Groningen

“The necessity of context in modeling complex emotion dynamics” (Abstract)

Laura Bringmann and her lab (LaBlab; Laura Bringmann’s intensive longitudinal data lab) aim to bridge the gap between different fields including statistics, philosophy, methodology and clinical psychology. Her research career has included prolonged stays at seven universities in four countries (The Netherlands, Germany, Belgium, USA). In 2019, she obtained a prestigious VENI grant focused on new network models to detect changes over time in psychiatric disorders. She is also part of the Stress in Action Gravitation/Zwaartekracht project (20 million €, 2022-2031; PI: Brenda Penninx) of the NWO, in which she combines machine learning and time series analyses. Laura Bringmann is also the initiator and organizer of the biannual Dutch-Belgian meeting “Dynamical Network and Time Series Models (DynaNeT)”, which brings together over 30 researchers from statistical and clinical fields.

Felix Schönbrodt, LMU Munich

“Reproducibility in methodological research: Modelling and improving epistemic uncertainty” (Abstract)

Felix Schönbrodt is professor at the psychological department of the Ludwig-Maximilians-University Munich, Germany. His research interests include implicit and explicit motives, dynamics in couple relationships, quantitative methods, and all issues revolving open science and the replicability of research. One special focus is to provide statistical packages in R and [interactive statistical web apps which can be used for teaching and for an enhanced understanding and usage of quantitative methods. Felix Schönbrodt is an initiator of the Commitment to Research Transparency, founding member of the German Reproducibility Network and managing director of the LMU Open Science Center.