Spring 2021 - BPK 304W D100

Inquiry and Measurement in Biomedical Physiology and Kinesiology (3)

Class Number: 7712

Delivery Method: In Person


  • Course Times + Location:

    Tu 8:30 AM – 11:20 AM

  • Exam Times + Location:

    Apr 24, 2021
    3:30 PM – 6:30 PM

  • 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.


There are 13 weeks of class.


The course includes three hours of LECTURE on Tuesdays (8:30 – 11:30 am) and one hour of LAB/TUTORIAL during scheduled times on Wednesday or Thursday.


Lectures will be SYNCHRONOUS; they will also be recorded and available on Canvas.


Labs/Tutorials will be SYNCHRONOUS; they will not be recorded.


Topics Covered


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, Regression, Lab Midterm 1 Review

Week 07 – Reading Week (NO CLASS)

Week 08 – Project Part II Instructions, Library Workshop, Lab Midterm 1

Week 09 – Nonparametric Statistics

Week 10 – Modeling, Abstracts

Week 11 – A/D Basics/Skinfold Compression

Week 12 - EMG, Making Data Stick, Lab Midterm 2 Review

Week 13 – Undergraduate Research Opportunities, Lab Midterm 2

Week 14 – Final Exam Review

Lab/Tutorials: Labs will be held in Bb Collaborate Ultra. Dr. Mackey will preview the labs during the last hour of Tuesday lectures. During the labs, The TA will address skills and evaluation procedures necessary for the completion of the tutorials. Numerous example data sets and analyses will be used. The instructions and data sets will be made available to students on Canvas on Tuesdays. Lab results sheets will be available on Canvas at the beginning of the first lab (Wednesdays) and must be completed and submitted by the end of the last lab (Thursdays). If you miss a lab, your score on the results sheet for that lab will be zero.

Lab Midterms: There will be two 45-minute practical exams held during your regularly scheduled lab time in weeks 8 and 13. Lab midterms will test the knowledge and skills acquired in the preceding labs.

Term Project

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 8 (1st submission). Project Part I will be graded and returned to the student in Week 10. The student will revise the Project Part I and re-submit it in Week 14 (2nd submission).

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 in Week 11 (1st submission). This will be graded and returned to the student in Week 13. The student will revise the Project Part II and re-submit it in Week 14 (2nd submission).

Late Penalties: A late penalty of 10% per day, including weekends and holidays, will be applied on all late assignments that are not supported with medical documentation. After 5 days the assignment will not be accepted. 

Attendance: Class attendance is strongly recommended. You are not required to attend class. Lecture recordings will be available on Canvas. 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 a university function (e.g., athletic competition), please let the instructor know within the first week of the semester.

Academic Integrity: Academic honesty is a condition of continued membership in the University community. Read the SFU policies (http://www.sfu.ca/policies/gazette/student.html) on cheating, plagiarism, and other forms of academic dishonesty. The consequences of such behavior are serious.

Also familiarize yourself with the SFU library tutorial on avoiding plagiarism. In order to review the Plagiarism Tutorial and take its quizzes, you will need to first log out of Canvas, or use a browser that is not currently logged into Canvas, and proceed to the link below (copy and paste it into the address bar): https://canvas.sfu.ca/courses/15986 

Note that they will not be evaluated (and so you do not need to submit anything), but it is expected that you will read and complete them.


At the end of BPK 304W, successful students will be able to:


  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, measures of variability, standardized scores (e.g., z scores), and percentiles.
  4. Explain the steps in inferential statistics to determine whether data support or refute 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 (weekly)


Lab Midterm 1


Lab Midterm 2


Project Part I


Project Part II


Final Exam



To complete the LABS/TUTORIALS, students will need Microsoft EXCEL and SPSS for data analysis. Students do NOT need to buy SPSS; rather, it will be available for download from SFU IT Services.



To complete the LABS/TUTORIALS, students will need Microsoft EXCEL and SPSS for data analysis. Students do NOT need to buy SPSS; rather, it will be available for download from SFU IT Services.


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 Canvas at no charge to the students.


Recommended readings will be posted throughout the semester on Canvas.

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://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


Teaching at SFU in spring 2021 will be conducted primarily through remote methods. There will be in-person course components in a few exceptional cases where this is fundamental to the educational goals of the course. Such course components will be clearly identified at registration, as will course components that will be “live” (synchronous) vs. at your own pace (asynchronous). Enrollment acknowledges that remote study may entail different modes of learning, interaction with your instructor, and ways of getting feedback on your work than may be the case for in-person classes. To ensure you can access all course materials, we recommend you have access to a computer with a microphone and camera, and the internet. In some cases your instructor may use Zoom or other means requiring a camera and microphone to invigilate exams. If proctoring software will be used, this will be confirmed in the first week of class.

Students with hidden or visible disabilities who believe they may need class or exam accommodations, including in the current context of remote learning, are encouraged to register with the SFU Centre for Accessible Learning (caladmin@sfu.ca or 778-782-3112).