Rate My Professor Caspar Van Lissa

Van Lissa’s research focuses on machine learning-informed theory construction, machine learning-informed research synthesis via meta-analysis and text mining systematic reviews, open science practices emphasizing computational reproducibility and workflow automation, adolescents’ socio-emotional development, parent-child conflict resolution, and fathers’ role in child development. He leads the INSIGHT lab, applying these approaches to fields like climate research, mental health, and well-being. Notable awards include the prestigious NWO Vidi grant (2024) for “From patterns to principles: using machine learning to construct social scientific theories,” an eScience Center spearhead grant, and the 2021 Teacher of the Year award from USocia for his Statistics 1 course at Liberal Arts and Sciences, which employs flipped classrooms, open science education, problem-based learning, and interactive eBooks. Key publications encompass “Toward a predictive model of moral concern” (Journal of Experimental Social Psychology, 2026), “Capturing causal claims: A fine-tuned text mining model for extracting causal sentences from social science papers” (Research Synthesis Methods, 2025), “Identifying prenatal risk factors of postpartum depression with machine learning” (Scientific Reports, 2025), “A novel, network-based approach to assessing romantic-relationship quality” (Perspectives on Psychological Science, 2024), and “A theory-informed emotion regulation variability index: Bray-Curtis dissimilarity” (Emotion, 2024). His contributions include developing free open-source research software and delivering workshops on open science.