Scott R. Colwell, PhD., has been an Associate Professor in the Department of Marketing and Consumer Studies since 2003 and an Adjunct Professor in the Department of Psychology since 2013. Prior to joining the University of Guelph, Dr. Colwell was a lecturer at Acadia University for 3 years and an executive in the financial services industry for 10 years.
Dr. Colwell has a PhD in Management and a post-graduate diploma in research methodology from the University of Bradford in the United Kingdom. His master's (MBA) and undergraduate work were both in strategy and finance. He holds the accreditation of Chartered Statistician (CStat) with the Royal Statistical Society and Professional Statistician (PStat) with the American Statistical Association.
Dr. Colwell's research interests include applied statistical analysis and social issues in management. He teaches courses in research methods and statistical analysis and consults in data analysis with researchers and students in a broad range of disciplines including the social, behavioural and health sciences.
Dr. Colwell's research interests include:
Applied Statistical Analysis:
1. Measurement invariance issues
2. Multivariate correction for attenuation
3. Latent variable modeling
4. Machine learning in social and behavioural methods
Social Issues in Management:
1. Ethical decision making
2. Social issues resulting from organizational decisions
3. Role of education in promoting ethical decision making
Dr. Colwell's primary teaching interests are in the area of applied statistics and research methods. Courses he has taught over the last ten years include:
PSYC*1010 Making Sense of Data
PSYC*2040 Research Statistics
MCS*3030 Research Methods
PSYC*3370 Experimental Design and Analysis
MCS*6050 Research Methods
MCS*6070 Structural Equation Modeling
MGMT*6830 Applied Univariate Statistics
PSYC*6380 Applied Multivariate Analysis
In addition, Dr. Colwell has taught a number of summer workshops for the University of Guelph in areas such as introductory and intermediate statistics, structural equation modeling, multilevel modeling, and growth modeling.