Overview

Description

CALENDAR DESCRIPTION:

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.

COURSE DETAILS:

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 (2^nd 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 (2^nd 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.

COURSE-LEVEL EDUCATIONAL GOALS:

At the end of BPK 304W, successful students will be able to:   Describe the steps in the scientific method. Describe characteristics of the Normal distribution and tests of normality. Calculate and interpret a variety of descriptive statistics including measures of central tendency, measures of variability, standardized scores (e.g., z scores), and percentiles. Explain the steps in inferential statistics to determine whether data support or refute a hypothesis. 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. Select, construct, and interpret appropriate statistical tests of association for normally distributed data including Pearson’s correlation and linear regression. Select, construct, and interpret appropriate non-parametric statistical tests including chi-square, rank order correlation, and binary logistic regression. Formulate a logical plan for data analysis that will adequately address a given research question including the expected output and interpretation of output. Identify the main steps and equipment used for basic examples of analog to digital data acquisition applications relevant to BPK. List and discuss basic techniques available for analysis of serial/longitudinal data relevant to BPK. List and discuss the general process of mathematical modeling with reference to specific examples relevant to BPK. Communicate scientific research questions, methods, results, and conclusions in writing in the form of a scientific journal article. Incorporate feedback on scientific writing to improve quality. Demonstrate competency in using software packages for quantitative data analysis, including Microsoft EXCEL and SPSS.

Grading

Materials

MATERIALS + SUPPLIES:

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.

REQUIRED READING:

RECOMMENDED READING: