Course coordinator
Professor Guy Wallis
Course overview
- Study period
- Semester 1, 2025 (24/02/2025 - 21/06/2025)
- Study level
- Undergraduate
- Location
- St Lucia
- Attendance mode
- In Person
- Units
- 2
- Administrative campus
- St Lucia
- Coordinating unit
- Human Movement & Nutrition Sci
Overview of the conduct of research in the field of human movement and nutrition sciences, principles of study design and analysis, critical review and interpretation of research. Practical skills will be developed in quantitative and qualitative analysis, and in the presentation of research findings.
The course provides an overview of how scientists conduct research in the field of human movement and nutrition sciences, outlining both the theoretical background and practical skills required .ﾠCourse content spans the nature of research and knowledge, the steps involved in development of a research question and the methods required to answer such questions. The course will also consider issues specific to conducting work with human participants including ethics and informed consent. The course discusses theﾠadvantages and disadvantages of various study designs includingﾠobservational, experimental, and qualitative research methods. The course also describes how research outcomes should be reportedﾠand presented in papers and talks.ﾠDescriptive and inferential statistics, including nonparametric techniques, will be undertaken using Graphpad Prism software.ﾠﾠﾠﾠﾠﾠﾠﾠﾠﾠ
Course contact
Course staff
Lecturer
Professor Guy Wallis
Dr Stephen Townsend
Facilitator
Ms Lee Svensson
Miss Ruth Brady
Ms Zoe Bauer
Miss Grace Ike
Timetable
The timetable for this course is available on the UQ Public Timetable.
Aims and outcomes
To provide students with the skills required to design and conduct a research project in the multi-disciplinary field of human movement and nutrition sciences
Learning outcomes
After successfully completing this course you should be able to:
LO1.
To describe and implement the steps involved in designing a hypothesis-driven research project involving human subjects
LO2.
To critically analyse research and research presentation within the broad field of human movement and nutrition sciences.
LO3.
To evaluate the effectiveness of qualitative research approaches for examining research questions.
LO4.
To apply, interpret and synthesise appropriate quantitative and statistical analyses for various study designs.
Assessment
Assessment summary
| Category | Assessment task | Weight | Due date |
|---|---|---|---|
| Project | Qualitative Research Assignment | 30% |
7/04/2025 2:00 pm |
| Examination |
Statistical Analysis Exam
|
20% |
8/05/2025 - 9/05/2025
Held in-person during timetabled practicals. |
| Examination, Quiz |
MCQ Statistics Exam
|
10% |
6/05/2025
Exam open over a 24hr period on the specified date. |
| Paper/ Report/ Annotation | Research Proposal | 40% |
30/05/2025 2:00 pm |
Assessment details
Qualitative Research Assignment
- Mode
- Written
- Category
- Project
- Weight
- 30%
- Due date
7/04/2025 2:00 pm
- Learning outcomes
- L01, L02, L03
Task description
You will be required to design and implement a qualitative research topic of your choice in an area relevant to human movement and/or nutrition.
- You will choose a topic in your field that you believe warrants critical, qualitative investigation.
- To deepen your understanding of this topic, you will conduct 2x pilot interviews with relevant subjects.
- Based upon the data gathered from these interviews, you will design a research question that interrogates a specific element of your chosen topic.
- You will design and describe research project that could feasibly answer your research question.
A full task description is available on Blackboard.
Use of generative Artificial Intelligence (AI) or Machine Translation (MT)
This task has been designed to be challenging, authentic and complex. Whilst students may use AI and/or MT technologies, successful completion of assessment in this course will require students to critically engage in specific contexts and tasks for which artificial intelligence will provide only limited support and guidance.
A failure to reference generative AI or MT use may constitute student misconduct under the Student Code of Conduct.
To pass this assessment, students will be required to demonstrate detailed comprehension of their written submission independent of AI and MT tools.
Submission guidelines
Submit a Word or PDF file via the relevant Turnitin submission portal on the course Blackboard site.
Deferral or extension
You may be able to apply for an extension.
The maximum extension allowed is 28 days. Extensions are given in multiples of 24 hours.
Late submission
A penalty of 10% of the maximum possible mark will be deducted per 24 hours from time submission is due for up to 7 days. After 7 days, you will receive a mark of 0.
Statistical Analysis Exam
- In-person
- Mode
- Activity/ Performance
- Category
- Examination
- Weight
- 20%
- Due date
8/05/2025 - 9/05/2025
Held in-person during timetabled practicals.
- Learning outcomes
- L04
Task description
Practical statistical exam which will pose questions similar to those seen during the prac classes. You will need to use GraphPad Prism to run this exam. Exam held during normal prac class hours.
Use of generative Artificial Intelligence (AI) or Machine Translation (MT)
This assessment task is to be completed in-person. The use of generative Artificial Intelligence (AI) or Machine Translation (MT) tools will not be permitted. Any attempted use of AI or MT may constitute student misconduct under the Student Code of Conduct.
Exam details
| Planning time | 10 minutes |
|---|---|
| Duration | 120 minutes |
| Calculator options | Any calculator permitted |
| Open/closed book | Open Book examination |
| Materials | Personal laptop |
| Exam platform | Other |
| Invigilation | Invigilated in person |
Submission guidelines
Deferral or extension
You may be able to defer this exam.
Your deferred exam date and time will be determined by the course coordinator and communicated to you via your UQ student email account.
Late submission
You will receive a mark of 0 if this assessment is submitted late.
MCQ Statistics Exam
- Online
- Mode
- Activity/ Performance
- Category
- Examination, Quiz
- Weight
- 10%
- Due date
6/05/2025
Exam open over a 24hr period on the specified date.
- Learning outcomes
- L04
Task description
This exam will test your knowledge of general statistical principles using multiple-choice answers. It will be similar in format to a set of online quizzes available through the Blackboard website.
Use of generative Artificial Intelligence (AI) or Machine Translation (MT)
This assessment task evaluates students' abilities, skills and knowledge without the aid of generative Artificial Intelligence (AI) or Machine Translation (MT). Students are advised that the use of AI or MT technologies to develop responses is strictly prohibited and may constitute student misconduct under the Student Code of Conduct.
Exam details
| Planning time | 10 minutes |
|---|---|
| Duration | 60 minutes |
| Calculator options | Any calculator permitted |
| Open/closed book | Open Book examination |
| Exam platform | Other |
| Invigilation | Not invigilated |
Submission guidelines
Deferral or extension
You may be able to defer this exam.
Your deferred exam date and time will be determined by the course coordinator and communicated to you via your UQ student email account.
Late submission
You will receive a mark of 0 if this assessment is submitted late.
Research Proposal
- Mode
- Written
- Category
- Paper/ Report/ Annotation
- Weight
- 40%
- Due date
30/05/2025 2:00 pm
- Learning outcomes
- L01, L02, L04
Task description
Write a research proposal for a research project of your choice, in an area relevant to human movement or nutrition sciences. The proposal should be written as if you were intending to apply for funding from a granting agency.
See Blackboard website for details.
Use of generative Artificial Intelligence (AI) or Machine Translation (MT)
This task has been designed to be challenging, authentic and complex. Whilst students may use AI and/or MT technologies, successful completion of assessment in this course will require students to critically engage in specific contexts and tasks for which artificial intelligence will provide only limited support and guidance.
A failure to reference generative AI or MT use may constitute student misconduct under the Student Code of Conduct.
To pass this assessment, students will be required to demonstrate detailed comprehension of their written submission independent of AI and MT tools.
Submission guidelines
Submit a Word or PDF file via the relevant Turnitin submission portal on the course Blackboard site.
Deferral or extension
You may be able to apply for an extension.
The maximum extension allowed is 28 days. Extensions are given in multiples of 24 hours.
Late submission
A penalty of 10% of the maximum possible mark will be deducted per 24 hours from time submission is due for up to 7 days. After 7 days, you will receive a mark of 0.
Course grading
Full criteria for each grade is available in the Assessment Procedure.
| Grade | Cut off Percent | Description |
|---|---|---|
| 1 (Low Fail) | 0 - 24 |
Absence of evidence of achievement of course learning outcomes. Course grade description: A cumulative score over the course of the semester equivalent to less than or equal to 24% |
| 2 (Fail) | 25 - 44 |
Minimal evidence of achievement of course learning outcomes. Course grade description: A cumulative score over the course of the semester equivalent to 25-44%. |
| 3 (Marginal Fail) | 45 - 49 |
Demonstrated evidence of developing achievement of course learning outcomes Course grade description: A cumulative score over the course of the semester equivalent to 45-49% |
| 4 (Pass) | 50 - 64 |
Demonstrated evidence of functional achievement of course learning outcomes. Course grade description: A cumulative score over the course of the semester equivalent to 50-64% |
| 5 (Credit) | 65 - 74 |
Demonstrated evidence of proficient achievement of course learning outcomes. Course grade description: A cumulative score over the course of the semester equivalent to 65-74% |
| 6 (Distinction) | 75 - 84 |
Demonstrated evidence of advanced achievement of course learning outcomes. Course grade description: A cumulative score over the course of the semester equivalent to 75-84% |
| 7 (High Distinction) | 85 - 100 |
Demonstrated evidence of exceptional achievement of course learning outcomes. Course grade description: A cumulative score over the course of the semester equivalent to 85% or greater |
Additional course grading information
A final percentage mark will be rounded to the nearest whole number (e.g., 64.50 and above will be rounded to 65 and 64.49 and below will be rounded down to 64.)
Supplementary assessment
Supplementary assessment is available for this course.
Additional assessment information
ASSIGNMNENT SUBMISSION
When submitting an assignment, remember to include a cover sheet, for example -ﾠ
- Assessment Task:ﾠ Assignment
- Course Title: Research Skills
- Course Code: HMST3846
- Student Name: Joe Bloggs
- Student Number: 4xxxxxxxx
If students experience difficulties submitting assessment tasks, they should (by the due date/time):
- Email a copy of the assessment task to the Course Coordinator. For contact details refer to Course Contributors section of the Course Profile.
- Include a screenshot of the error message.
What is Turnitin
Turnitin is an electronic assignment submission tool. The tool provides your Course Coordinator with:
· a record of the exact submission time of an assignment
· an originality report indicating the percentage of your work that is an exact match of existing materials within the Turnitin database.
Instructions on how to submit an assignment using Turnitin are located on the UQ Library website
Submit your Turnitin assignment - Library Guide
Note:
When submitting, to check that you have chosen the correct file on the Preview Submission page and click on the Submit to Turnitin button. ﾠ
Remember to download your digital receipt in your Assignment inbox to confirm successful submission.
If a submission cannot be successfully completed, email a copy of the assessment task to the Course Administrator. For contact details refer to Course Contributors section of the Course Profile.
Learning resources
You'll need the following resources to successfully complete the course. We've indicated below if you need a personal copy of the reading materials or your own item.
Library resources
Library resources are available on the UQ Library website.
Additional learning resources information
The course Blackboard website will be used to provide online example statistical quizzes relevant to the statistical exams.
Please follow the instructions on Blackboard to obtain a licensed copy of GraphPad Prism on your personal laptop.
Learning activities
The learning activities for this course are outlined below. Learn more about the learning outcomes that apply to this course.
Filter activity type by
Please select
| Learning period | Activity type | Topic |
|---|---|---|
Week 1 |
Lecture |
Introduction to research in HMNS Overview of the course. How we come to know through research. What is research? Learning outcomes: L01 |
Week 2 |
Lecture |
What is it you want to know? Knowledge, truth and research; Paradigm debates and issues. Classifying research. Learning outcomes: L01 |
Week 3 |
Lecture |
Principles in research design Developing the research problem. Choosing the appropriate methodology. Learning outcomes: L01 |
Workshop |
Qualitative Assignment workshop Practice conducting and interpreting interviews. Learning outcomes: L02, L03 |
|
Week 4 |
Lecture |
Why and when would you need to use statistics? Becoming acquainted with statistical concepts including sampling, measures of central tendency, and variability. Learning outcomes: L02, L04 |
Practical |
An introduction to GraphPad and descriptive statistics Introduction to stats package Prism. Learning outcomes: L04 |
|
Week 5 |
Lecture |
Experimental design Validity and reliability. Sample size calculations. Categorical data, rates and risk. Learning outcomes: L04 |
Practical |
Categorical data, reliability, sample size Learning outcomes: L04 |
|
Week 6 |
Lecture |
Relationships among variables Correlation and regression. Learning outcomes: L04 |
Practical |
Correlation and regression Learning outcomes: L04 |
|
Week 7 |
Lecture |
Differences between and among groups (parametric tests) Independent and dependent t tests (one-tailed vs. two-tailed tests), and ANOVA. Learning outcomes: L04 |
Practical |
Parametric group tests (t tests and ANOVA) Learning outcomes: L04 |
|
Week 8 |
No student involvement (Breaks, information) |
Easter Break No lecture or practical classes |
Mid-sem break |
No student involvement (Breaks, information) |
Mid-Semester Break No lecture or practical classes |
Week 9 |
Lecture |
Multivariate analysis and non-parametric analysis ANOVA for more than one variable. Alternative, non-parametric tests Learning outcomes: L04 |
Practical |
Multivariate analysis, non-parametric approaches Learning outcomes: L04 |
|
Week 10 |
Practical |
Statistical Analysis Test Practical statistics test in prac class. Note you must attend this invigilated exam session in person. Learning outcomes: L04 |
Not Timetabled |
MCQ Stats Quiz Online MCQ statistics test run remotely. Exam open for 24hr period from midnight. Learning outcomes: L04 |
|
Week 11 |
Workshop |
Human ethics workshop Learn about ethics in preparation for your upcoming Research Proposal Assignment. Learning outcomes: L01, L03 |
Lecture |
Reporting research (the good and the bad) Understanding the process of peer review. Common mistakes in statistics. Scientific misconduct. Learning outcomes: L02 |
Policies and procedures
University policies and procedures apply to all aspects of student life. As a UQ student, you must comply with University-wide and program-specific requirements, including the:
- Student Code of Conduct Policy
- Student Integrity and Misconduct Policy and Procedure
- Assessment Procedure
- Examinations Procedure
- Reasonable Adjustments - Students Policy and Procedure
Learn more about UQ policies on my.UQ and the Policy and Procedure Library.
School guidelines
Your school has additional guidelines you'll need to follow for this course: