معرفی
Jacob Coutts is a Lecturer in the Social Data Science Major and Department of Psychology at the University of Maryland. He holds a PhD and MS in Quantitative Psychology and MS in Applied Statistics from The Ohio State University. His research focuses on advancing statistical methods for mediation, moderation, and conditional process analysis, emphasizing accessibility through software development (e.g., R packages). He leads the Computational and Statistical Inference (CSI) Lab, which prioritizes mentorship, methodological innovation, and programming proficiency (R/Python).
Education:
- PhD in Quantitative Psychology, The Ohio State University (2023)
- MS in Quantitative Psychology, The Ohio State University
- MS in Applied Statistics, The Ohio State University
- BS in Psychological Sciences, Northern Arizona University
Research Interests:
- Mediation & moderation analysis
- Resampling methods
- Statistical power analysis
- Data visualization
- Dyadic data analysis
- Human sexuality
Recent work includes presentations on quantitative pedagogy and DEI strategies in methodology courses, as well as collaborative projects on autism screening via machine learning and neurodegenerative disease research. His team’s 2024 projects explored bootstrap applications, workplace wellness, and ADHD diagnosis methods (winning an Outstanding Research Award).
Teaching: He instructs courses ranging from introductory psychology to advanced topics like mediation analysis and survey data science, prioritizing programming skills (R/Python) and practical statistical literacy.
