May contain alternative delivery sections; refer to the Tuition and Fees page in the University Regulations section of the Calendar.
The purpose of this course is to present students with a variety of educational data mining techniques, with an emphasis on conceptual understanding and applications. Students will also learn how to implement these techniques with statistical software such as R or Python. This course is open to graduate students across the campus, with priority given to the Faculty of Education graduate students.
This course will focus on the analysis of data from experiments and surveys using the analysis of variance. Students will develop knowledge of and skills in understanding the underlying statistical models, matching statistical models to research designs, using computer software to conduct appropriate statistical analyses, and interpreting and reporting findings. Prerequisites: EDPY 500 or equivalent.
This course will introduce students to advanced statistical techniques that are frequently used in data analysis in the social sciences. Selected topics such as multiple regression, MANOVA, canonical correlation, principal component analysis, and factor analysis will be covered. Prerequisite: EDPY 505 or equivalent.
Cui, Y., Gierl, M. J., & Chang, W. W.
Journal of Educational Measurement. 2012 January; 49
Cui, Y. & Li, J.C.
Journal of British Mathematical and Statistical Psychology. 2012 January;
Canel-Çınarbaş, D., Cui, Y., & Lauridsen, E.
Measurement and Evaluation in Counseling and Development. 2011 January; 44
Li, J. C., Chan, W. & Cui. Y.
Journal of British Mathematical and Statistical Psychology. 2010 January;
Li, J. C.-H., Cui, Y., & Chan, W.
Journal of Applied Psychology.