In this course, students learn advanced data analytic techniques for psychological research. Topics include advanced analyses within the general linear model (GLM), e.g., multiple and logistic regression, as well as special applications of the GLM such as ANOVA. Students continue to gain experience in computer-based analytic methods and coding techniques.

Antirequisite(s): the former Psychology 2810; the former Psychology 2820E.

Prerequisite(s): At least 70% in Psychology 2811A/B; at least 60% in Data Science 1000A/B and at least 60% in 0.5 credit of Year 1 Math from among the following courses: Calculus 1000A/B, Calculus 1301A/B, Calculus 1500A/B, Calculus 1501A/B, Mathematics 1225A/B, Mathematics 1228A/B, Mathematics 1229A/B, Mathematics 1600A/B, or Applied Mathematics 1201A/B, or registration in Year 2 of an Honours Specialization in Neuroscience with special permission from the program administrator. Math 1228A/B is recommended. Students who have completed Statistics 1024A/B (or other Year 1 introductory statistics course in addition to 0.5 credit of Year 1 Math) instead of Data Science 1000A/B may enrol after completing an introductory programming class from the following list: Computer Science 1025A/B, Computer Science 1026A/B, Computer Science 2120A/B, Data Science 1200A/B, Digital Humanities 2220A/B, or Engineering Science 1036A/B. Data Science 2000A/B may be substituted for Data Science 1000A/B for students entering the program with 1.0 credits of Year 1 Math courses.

Extra Information: 2 Lecture Hours; 2 Laboratory Hours. Enrollment in this course will be by special permission only. Restricted to Psychology programs, Honours Developmental Cognitive Neuroscience, Psychology special students and Scholars Electives.