Fall 2019 - PSYC 301 D100
Intermediate Research Methods and Data Analysis (4)
Class Number: 9886
Delivery Method: In Person
Course Times + Location:
Mo 12:30 PM – 2:20 PM
WMC 2202, Burnaby
Exam Times + Location:
Dec 12, 2019
12:00 PM – 2:00 PM
BLU 10011, Burnaby
1 778 782-5685
Prerequisites:PSYC 201 and 210 and a minimum CGPA of 2.67.
A continuation of PSYC 201 and 210. Provides extensions of the basic theory and methods of research design and data analysis. Includes discussions of the analysis of substantive problems, the choice of appropriate research designs, and special problems that arise in the analysis of psychological data. Quantitative.
This course is about statistics, data analysis and related matters. Why do we spend time on this material? Because
High quality empirical research in the social sciences is possible only through a thorough understanding of statistics/data analysis.
Statistics is difficult.
Most of the difficulty arises as a result of a dense terminology, and the need for precise, logical thought.
Aim of Course:
To develop a competency in thinking about certain statistical issues as they arise in psychology/An understanding of what you, and others, are doing when doing statistics.
Note: A deep understanding of mathematics is not required. The ability to think logically is required.
Note: You will not walk out of this course a master data analyst. Mastery takes years.
If you do well in Psyc301, you should take Psyc410 (advanced ANOVA) and Psyc411 (multivariate analysis). Make no mistake: Honours theses involve complicated analyses and if you are planning to do an honours, you will thank yourself for taking the time to gain a decent training in statistical methodology.
This is not an empirical fact based course. Sometimes there is no simple answer.
Practical Details You are expected to know the material presented in lecture and tutorial. This material is supported by various other sources, notably readings that will be placed in the copier room on an as needed basis and outlines handed out in class.
Assignments: You will carry out SPSS analyses and answer questions. Although the assignments are more applied in nature, I will, occasionally, include a logical question. These assignments are supposed to be challenging. You will be working with real data and typically inventing solutions to tricky problems. How you state your conclusions does matter (i.e, competent writing will be rewarded).
Note: Late assignments will receive a mark of zero.
Exams: 1 Midterm (October 28), 1 Final (December 12)
- 3 to 5 short essay.
- How you state your opinions matters.
- The final is not cumulative
- The assignment of marks will be according to the following scheme:
- 4 Assignments: 40%
- 1 Midterm: 30%
- 1 Final: 30%
The grade categories are as follows:
Sept 9. Lect1 Data Analysis and Logic of Statistical Inference (Assignment 1 out)
Sept 16. Lect2 Logic of Statistical Inference
Sept 23. Lect3 Logic of Statistical Inference
Sept 30. Lect4 Logic of Statistical Inference (Assignment 2 out)
Oct 7. Lect5 directionality , illogicalities (Assignment 1 due)
Oct 14. Thanksgiving. no class.
Oct 21. Lect6 Concept of Relationship
Oct 28. Lect7 Midterm
Nov 4. Lect8 1-factor b.s. design (relationship between continuous d.v. and nominal i.v.)(Assignment 3 out)
Nov 11. Remembrance Day. no class. (Assignment 2 due by Wednesday, 13th, 4:00p.m., RCB4202)
Nov 18. Lect 9 relationship for 2 QC variables (Assignment 4 out)
Nov 25. Lect 10 p-way b.s. design (relationship between continuous d.v. and set of nominal i.v.s) (Assignment 3 due)
Dec 2. Lect 11
December 10. Assignment 4 due: 4:00p.m., RCB4202
December 12. Final Exam. 12-3
SFU’s Academic Integrity web site http://www.sfu.ca/students/academicintegrity.html is filled with information on what is meant by academic dishonesty, where you can find resources to help with your studies and the consequences of cheating. Check out the site for more information and videos that help explain the issues in plain English.
Each student is responsible for his or her conduct as it affects the University community. Academic dishonesty, in whatever form, is ultimately destructive of the values of the University. Furthermore, it is unfair and discouraging to the majority of students who pursue their studies honestly. Scholarly integrity is required of all members of the University. http://www.sfu.ca/policies/gazette/student/s10-01.html
ACADEMIC INTEGRITY: YOUR WORK, YOUR SUCCESS