I am a Lecturer in the Social Data Science Major and Department of Psychology at the University of Maryland. Go Terps! Previously, I earned my Ph.D. in Quantitative Psychology and MS in Applied Statistics from The Ohio State University in 2023 where I studied mediation (indirect effects), moderation (conditional effects), dyadic data, resampling methods, statistical power analysis, and data visualization. I have held numerous positions in industry and academia that have given me a unique approach to and view of research methods and statistics. Statistics is the lens by which I view the world and I hope to instill the same mindset in others! Learn more about me below:
Teaching
I am a firm believer in the power of teaching and I bring my user-focused research philosophy into the classroom often. I am committed to training the next generation of researchers by incorporating active learning (e.g., live coding sessions), instilling a firm foundation in programming skills in contemporary languages (e.g., R, Python), and teaching my students to think critically about science they and others are doing by highlighting the open science movement. (See below for more about the classes I am or will be teaching!)
CSI Lab
I am the head of the Computational and Statistical Inference (CSI) Lab (learn more in the research tab). If you want to improve or apply complex statistical methods in a research setting, then join the team! (Graduate and undergraduate students welcome.)
Research
My research philosophy is to make science more rigorous through the development and improvement of statistical methods. I am also a believer in making science and statistics more accessible, and as such I create packages that make my methodological developments easier to implement and write accompanying tutorial or pedagogical articles. See more about my work in the research tab!
Classes I have taught at the University of Maryland include
- PSYC489K - Mediation, Moderation, and Conditional Process Analysis
- SURV400 - Fundamentals of Survey and Data Science
- BSOS233 - Data Science for the Social Sciences
- BSOS180 - Introduction to Data Management
- PSYC417 - Data Science for Psychology and Neuroscience Majors
- PSYC300 - Research Methods in Psychology
- PSYC479 - Special Research in Psychology (RAs)
- PSYC478 - Independent Study in Psychology (UTAs)
- PSYC100 - Introduction to Psychology
Areas of Interest
- Mediation, moderation, and conditional process analysis
- Resampling methods
- Statistical power analysis
- Data visualization
- Dyadic data analysis
- Human sexuality
Degrees
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PhDQuantitative Psychology, The Ohio State University
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MSQuantitative Psychology, The Ohio State University
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MSApplied Statistics, The Ohio State University
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BSPsychological Sciences, Northern Arizona University
The Computational and Statistical Inference (CSI) Lab is based on three pillars: mentorship (professional development), research (methodological or substantive) and programming (e.g., R, Python). We take on a variety of projects and are always open to consultation or collaborations. See more about the team below:
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Jacob Coutts, PhD, MAS - Head of the CSI Lab Jacob does research on statistical methods and creates packages to make these methods more accessible to applied researchers. He is interested in mediation & moderation analysis, power analysis, dyadic data, resampling methods, and data visualization. He has a passion for collaboration and mentorship. He also loves sports, stand-up, and movies. Contact: jjcoutts [at] umd.edu |
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Irene Navaleza - Lab Manager Irene is interested in cognitive psychology, and particularly in late-onset neurodevelopmental conditions. After graduation, she wants to start a career in data science/analytics before returning higher education (as long as funding complies). When not in the lab, you can find her rock climbing, doing arts and crafts, or playing board games! Contact: irenenav [at] terpmail.umd.edu |
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Katy Lamb - 4th Year Social Data Science Student (Psychology Track) Katy is interested in conducting research in developmental psychology and neuroscience. Specifically, she is interested in neurodivergence in children and adolescents. After graduation, she wants to continue conducting research, whether that be as a lab manager or data scientist (TBD). She is excited to be in the lab and build her skills in statistical analysis and data processing! Contact: katylamb [at] terpmail.umd.edu |
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Salma Younis - 4th Year Neuroscience Student (Human Development Minor) Salma is interested in behavioral and cognitive neuroscience, specifically in treatment for neurodegenerative disease and adult psychopathology. After graduation, she wants to continue research and develop health intervention plans while pursuing higher education. She is an avid puzzler (self-proclaimed crossword expert), ice hockey lover, and is learning to draw/paint! Contact: syounis [at] terpmail.umd.edu |
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Dorsa Aflaki - 4th Year Psychology Student Contact: daflaki [at] terpmail.umd.edu |
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Hana Lee - 2nd Year Social Data Science Student Hana is interested in social science research regarding public policy and social inequality. After graduation, she wants to work as a data scientist and continue to do research. She is looking forward to being a part of the lab and is excited to develop her knowledge in data analysis and statistics.
Contact: hlee1241 [at] terpmail.umd.edu |
Recent projects the CSI lab has worked on...
Lee, H., Navaleza, I., & Coutts, J.J. (2024). Addressing the replication crisis: Can we close the gap with the bootstrap? Poster presented at the Student Undergraduate Research Conference. College Park, MD, 19 July 2024.
Lamb, K., Navaleza, I., & Coutts, J.J. (2024). From data to diagnosis: Machine learning advancements in autism screening. Poster presented at the Student Undergraduate Research Conference. College Park, MD, 19 July 2024.
Younis, S., Navaleza, I., & Coutts, J.J. (2024). Exploring non-motor symptoms in motor neuron disease. Poster presented at the Student Undergraduate Research Conference. College Park, MD, 19 July 2024.
Aflaki, D., Navaleza, I., & Coutts, J.J. (2024). Workplace wellness: Work stressors mediates the relationship between stress management programs and mental well-being. Poster presented at the Student Undergraduate Research Conference. College Park, MD, 19 July 2024.
Coutts, J.J. (2024). Course correction: A call for more effective quantitative pedagogy. Invited talk presented at the Rocky Mountain Methodology Academy. Calgary, CA, 17 July 2024.
Coutts, J.J. (2024). Research-supported approaches to addressing DEI in a methods-oriented class Poster presented at the Innovations of Teaching and Learning Conference, College Park, MD, 10 May 2024.
Navaleza, I., & Coutts, J.J. (2024). I’m worried I can’t do it all: Examining self-efficacy as a mediator between burnout and anxiety. Poster presented at the Psychology Undergraduate Research Day, College Park, MD, 22 April 2024.
Navaleza, I., & Coutts, J.J. (2024). Attention! Data Helps Diagnoses: A machine learning approach to predicting ADHD. Poster presented at the University of Maryland Research Fair, College Park, MD, 17 April 2024. (Outstanding research award winner.)
