thill001@dundee.ac.uk
Dr. Taylor G. Hill is the director of the Socio-Ecological Well-Being Research Lab at the University of Dundee in Scotland.
She completed her PhD in Experimental Psychology from Dalhousie University in Halifax, Nova Scotia, where she developed expertise in using statistical techniques that identify well-being promoting contexts (e.g., personality-driven processes which contribute to happiness).
During her doctoral training, she was the lead analyst for the largest single non-government well-being dataset in Canada (The 2019 Nova Scotia Quality of Life Initiative) where she fell in love with population survey methods, open science, and data visualization.
She is an Affiliate Researcher with the Canadian Alliance for Social Connection and Health and Wellstream: Canadian Centre for Innovation in Child & Youth Mental Health & Substance Use, and the Research Methods Consultant with Engage Nova Scotia.
Three weeks after defending her dissertation, she created Pear Tree Stats and moved to Scotland to join the Psychology faculty at the University of Dundee. As a Lecturer and director of the SEW lab, she continues studying well-being at both the individual level (positive psychology) and population level (community mental health promotion).
Perhaps most importantly, she is a triplet, a tabby cat mom, knows every episode of Friends, and loves live music, traveling, coffee, and reading historical fiction.
https://www.dundee.ac.uk/people/taylor-hill
Kavya Ramchandran
Kendall Smith
Aimee Allan
Samuel Trayner
Tamzyn Harpur
Akanksha Joshi
Will Clarkson
Neela Hossain
Ava MacFarlane Cifuentes
The lab is currently being established; please reach out for opportunities to join.
I am happy to recieve inquiries from potential research students (both undergraduate and postgraduate level) who are interested in well-being research. I am also happy to hear from those interested in post-doctoral opportunities, particularly those who are interested in quantitative methods in positive psychology (e.g., using R to analyze existing or new data).