Fall 2018 - BPK 304W D100

Inquiry and Measurement in Biomedical Physiology and Kinesiology (3)

Class Number: 4911

Delivery Method: In Person


  • Course Times + Location:

    Sep 4 – Dec 3, 2018: Tue, 2:30–4:20 p.m.

    Sep 4 – Dec 3, 2018: Thu, 2:30–3:20 p.m.

  • Exam Times + Location:

    Dec 5, 2018
    Wed, 3:30–6:30 p.m.

  • Prerequisites:

    BPK 142, 201, 205, and STAT 201.



This course covers the evaluation of measurement quality, test construction and assessment, and computer techniques for data capture and signal processing relevant to issues in Biomedical Physiology and Kinesiology. Prereq statistical knowledge will be put into practice when discussing typical research designs, modeling and hypothesis testing in Biomedical Physiology and Kinesiology. Students with credit for BPK 304 may not repeat this course for further credit. Writing/Quantitative.


Schedule of Topics
Week 01 - Course Introduction and Scientific Method
Week 02 - Normal Distribution and Descriptive Statistics
Week 03 - Inferential Statistics and Differences between Means I (T-Tests)
Week 04 - Differences between Means II (ANOVA)
Week 05 - Differences between Means III (ANCOVA) and Project Part I Instructions
Week 06 - Correlation and Regression
Week 07 - Project Part II Instructions/Library Workshop/Lab Midterm #1
Week 08 - Nonparametric Statistics
Week 09 - Modeling
Week 10 - A/D Basics/Skinfold Compression/Abstracts
Week 11 - EMG/Making Data Stick
Week 12 - Course Review/Lab Midterm #2 Review
Week 13 - Return Project Part II/Return Lab Midterm #2/ Undergrad Research


  1. Describe the steps in the scientific method.
  2. Describe characteristics of the Normal distribution and tests of normality.
  3. Calculate and interpret a variety of descriptive statistics including measures of central tendency, measure of variability, standardized scores (e.g., z scores), and percentiles.
  4. Explain the steps in inferential statistics to determine whether data support or refutes a hypothesis.
  5. Select, construct, and interpret appropriate statistical tests for differences between means, including independent samples t-tests, paired samples t-tests, analysis of variance (randomized groups, repeated measures), and analysis of covariance.
  6. Select, construct, and interpret appropriate statistical tests of association for normally distributed data including Pearson’s correlation and linear regression.
  7. Select, construct, and interpret appropriate non-parametric statistical tests including chi-square, rank order correlation, and binary logistic regression.
  8. Formulate a logical plan for data analysis that will adequately address a given research question including the expected output and interpretation of output.
  9. Identify the main steps and equipment used for basic examples of analog to digital data acquisition applications relevant to BPK.
  10. List and discuss basic techniques available for analysis of serial/longitudinal data relevant to BPK.
  11. List and discuss the general process of mathematical modeling with reference to specific examples relevant to BPK.
  12. Communicate scientific research questions, methods, results, and conclusions in writing in the form of a scientific journal article.
  13. Incorporate feedback on scientific writing to improve quality.
  14. Demonstrate competency in using software packages for quantitative data analysis, including Microsoft EXCEL and SPSS.


  • Lab Results Sheets (handed out at each lab) 4%
  • Lab Midterm 1 (Week 7) 6%
  • Lab Midterm 2 (Week 12) 5%
  • Project Part I (Results Section), 1st Submission (Week 7) 10%
  • Project Part 1 (Results Section), 2nd Submission (before/at Final Exam) 5%
  • Project Part II (Complete Journal Article), 1st Submission (Week 11) 30%
  • Project Part II (Complete Journal Article), 2nd Submission (before/at Final Exam) 5%
  • Final Exam 35%


Lectures and Lab Tutorials: The course includes two hours of lecture on Tuesdays, one hour of lecture on Thursdays, and one hour of tutorial in a computer teaching lab on Thursdays.  

Lab Tutorials: Lab Tutorials will be held in a University Computer Teaching Lab. The T.A. will address skills and evaluation procedures necessary for the completion of the tutorials. Numerous example data sets and analyses will be used. Lab results sheets will be handed out at the beginning of lab. These sheets will be completed during the lab and submitted before leaving at the end of the lab. These sheets will not be handed out before the beginning of your registered lab.  

Lab Midterms: There will be two 45-minute practical exams held during your regularly scheduled lab time in weeks 7 and 12. Lab midterms will test the knowledge and skills acquired in the preceding labs.  

Project Part I: Students will be required to write the Results section of a journal article based on the results obtained from the data analysis completed in the Week 5 lab. This writing assignment will be given in Week 5 and is due in Week 7 (1st submission). Project Part I will be graded, edited, and returned to the student in Week 9. The student will revise the Project Part I and re-submit it for 2nd submission before/at the final exam.  

Project Part II: Building on the Results section completed for Project Part I, students will be required to write a complete journal article one would submit to a scientific journal. Project Part II will be due for 1st submission in Week 11. This will be edited and returned to the student in Week 13. The student will revise the Project Part II and re-submit it for 2nd submission before/at the final exam.  

Attendance: Class attendance is strongly recommended. You are not required to attend class. However, if you choose not to attend, do not expect the instructor to repeat announcements, to loan you her lecture materials, or to give you any handouts that might have been distributed. If you know that you are going to miss a class, you might want to ask a classmate to pick up materials for you and to borrow her notes. If extenuating circumstances (unusual circumstances beyond your control, such as death of a close family member or severe illness) cause you to miss a lab or other marked activity, you should contact the instructor to make alternate arrangements as soon as you can. If you will need to miss class for a religious holiday or travel related to a university function (e.g., athletic competition), please let the instructor know within the first week of the semester.  

The procedure to be followed if you miss class work due to illness is explained at the following link. https://www.sfu.ca/students/health/resources/faq/sick-notes.html  

If a medical note is appropriate, please bring a Health Care Provider Statement to your physician for completion, especially if it is required for labs, midterms, the project, and final exam. The completed note should state the limitations caused by your sickness (e.g. requires bed rest, will not be able to sit for extended periods, cannot concentrate etc.)  

The Health Care Provider form is available at the following link: https://www.sfu.ca/content/dam/sfu/students/health/pdf/SSFU%20HCS%20Ad12120314070.pdf

Academic Honesty and Student Conduct: Academic honesty is a condition of continued membership in the University community. Academic dishonesty, including plagiarism or any other form of cheating is subject to serious academic penalty, i.e., failure on an assignment, failure in a course, suspension or expulsion from the University. The University codes of student conduct and academic honesty are contained in policies T10.01 and T10.02, which are available at http://www.sfu.ca/policies/gazette/teaching.html



Inquiry & Measurement in Kinesiology, Ward & Mackey 2013. This is a custom text written specifically to cover the diverse topics in this course. Chapters will be posted as .pdf files on the course website at no charge to the students.


Recommended readings will be posted throughout the semester on the course website.

Department Undergraduate Notes:

It is the responsibility of the student to keep their BPK course outlines if they plan on furthering their education.

Registrar Notes:

SFU’s Academic Integrity web site http://students.sfu.ca/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