Researchers at 不良研究所 aspire to be world-leaders in the development, evaluation, and application of models and methods for the analysis of psychological data. Training in Quantitative Psychology and Modelling is research intensive, with students working closely with one or more faculty members. Students have opportunities to share their research with colleagues in the area,听present at a number of听professional conferences, and gain听hands-on听teaching experience. The ultimate goal is to train students to be independent and outstanding quantitative data analysts, who are highly sought after by both academic and industrial institutions.
To read more about Quantitative Psychology and Modelling and a career in this field, see the links on this page.
Applying to the PhD Program
Prospective students interested in studying Quantitative Psychology and Modelling generally apply to the PhD program (/psychology/graduate) and most students follow the 鈥淓xperimental Psychology鈥� track. As the PhD program is mentorship-based, it is recommended to learn more about each faculty member鈥檚 research interests to select an appropriate supervisor in your application. However, faculty members in the area often collaborate on research projects and sometimes co-supervise students. Prospective students are encouraged to list all quantitative faculty as possible supervisors on the application summary sheet in order of preference, with the personal statement more clearly outlining specific research interests and a proposed supervisor.
Preparation
Preparation for graduate studies in Quantitative Psychology is often supervisor and research topic dependent. Coursework from advanced undergraduate courses in applied statistics in psychology, or undergraduate courses or research experiences acquired in statistics or computer science present transferrable background skills but are not necessary prior to starting graduate studies.
Curriculum
PhD students following the Experimental Psychology track generally must fulfill or be exempted from (based on prior coursework) two introductory statistics course requirements on basic statistics such as ANOVA and multiple linear regression as well as a sampling of multivariate techniques (PSYC 650/651). Aside from these and two professional development courses (PSYC 660D1/660D2), students take a minimum of 4 area seminars, 2 of which must be within the department. Area seminars in quantitative psychology generally cover modelling techniques such as Multilevel Modeling, Structural Equation Modeling, Applied Bayesian Statistics, Measurement, Item Response Theory, and Machine Learning. Students in the PhD program are encouraged to take complementary courses from other departments (Biostatistics, Math/Stats, and Computer Science) such as Causal Inference, Generalized Linear Models, Machine Learning, Nonparametric Statistics, Mathematical Statistics, Statistical Inference, Optimization, Natural Language Processing, and so on. Quebec universities also enjoy a collaborative policy: Should another course also be available at another public university in Montreal (e.g., Concordia, Universite de Montreal, Universite du Quebec), attendance of that course is also a possibility (/transfercredit/iut).
To find out more about the formal coursework requirements, see here for information about the PhD program and refer to the Clinical or Experimental links (/psychology/graduate/program-tracks), refer to the Graduate Student Handbook on this page (/psychology/graduate/current-students), or the official PhD program requirements at the registrar (/study/).
Faculty Members
Core Faculty
Carl F. Falk - Faculty Profile,
Heungsun Hwang - Faculty Profile,
Milica Mio膷evi膰 - Faculty Profile,
Faculty Lecturer
Jens Kreitewolf -听Faculty Profile,
Affiliated Faculty
Brendan Johns - Faculty Profile,
Tom Shultz听- Faculty Profile,
Resources
The research environment and facilities at 不良研究所 provide ample opportunities for collaboration, professional development, and computationally intensive research.
The Computational and Data Systems Initiative (/cdsi/) offers additional research collaboration, training, and teaching/consulting opportunities. Additional training and talks are sometimes available through Mila ().
不良研究所 quantitative methods faculty and students also enjoy accesses to high performance computing clusters hosted by Calcul Qu茅bec () / Compute Canada ().
Recent Graduates
Gyeongcheol Cho (PhD, 2023)听
- Assistant Professor at the Ohio State University
Sunmee Kim (PhD, 2020)
- Assistant professor at University of Manitoba, Canada
Ji Yeh Choi (PhD, 2017)
- Assistant professor at York University, Canada
Ahmed Khalil Ben Ayed (Post-doctoral, 2017)
- Assistant Professor at University of Ottawa, Canada
Jyungkyu Park (PhD, 2016)
- Assistant Professor at Kyungpook National University, South Korea
Tian-Yu Tan (PhD, 2014)
- Vice President of Quantitative Equity Portfolio Management and Research, TD Asset Management
Lixing Zhou (PhD, 2014)
- Commercial Underwriter, OnDeck Canada
H茅la Romdhani (Post-Doctoral, 2014)
- Associate Consultant, Analysis Group, Canada
Hye Won Suk (PhD, 2013)
- Associate Professor at Sogang University, South Korea
Kwanghee Jung (PhD, 2011)
- Assistant Professor at Texas Tech University, USA
Related Professional Societies/Meetings
- American Psychological Association 鈥� Division 5 ()
- Psychometric Society ()
- Modern Modeling Methods ()
- National Council on Measurement in Education ()
- Cognitive Science Society ()
- Statistical Society of Canada ()
- Quantitative Methods section of the Canadian Psychological Association ()
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