Course coordinator
Professor Guy Wallis
Course overview
- Study period
- Semester 2, 2025 (28/07/2025 - 22/11/2025)
- 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, outliningﾠboth the theoretical background and practical skills required.ﾠCourse content spans the nature of research and knowledge, the steps involved in developing 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 the advantages and disadvantages of various study designs includingﾠobservational, experimental, and qualitative research methods. The course also describes how research outcomes should be reportedﾠin scientific papers and talks.ﾠDescriptive and inferential statistics, including nonparametric methods, will be undertaken as part of this course.
Course contact
Course staff
Lecturer
Professor Guy Wallis
Dr Stephen Townsend
Facilitator
Ms Lee Svensson
Miss Lucy Wilson
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% |
8/09/2025 2:00 pm |
| Examination |
Statistical Analysis Exam
|
30% |
8/10/2025 - 10/10/2025
Held in-person during timetabled practicals. |
| Examination, Quiz |
MCQ Statistics Exam
|
Pass/Fail |
8/10/2025
Held online during timetabled lecture. |
| Paper/ Report/ Annotation | Research Proposal | 40% |
31/10/2025 2:00 pm |
A hurdle is an assessment requirement that must be satisfied in order to receive a specific grade for the course. Check the assessment details for more information about hurdle requirements.
Assessment details
Qualitative Research Assignment
- Mode
- Written
- Category
- Project
- Weight
- 30%
- Due date
8/09/2025 2:00 pm
- Learning outcomes
- 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 a research project that could feasibly answer your research question.
Submission guidelines
To be submitted via the relevant Turnitin submission portal on the course Blackboard site.
When submitting an assignment, remember to include a cover sheet, for example -ﾠ
- Assessment Task: Assignment
- Course Title: Research Skills
- Course Code: HMST3846
- Student Name: Guy Wallis
- Student Number: 4xxxxxxxx
- Prac Group: P01
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.
- Include a screenshot of the error message.
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
- Written
- Category
- Examination
- Weight
- 30%
- Due date
8/10/2025 - 10/10/2025
Held in-person during timetabled practicals.
- Other conditions
- Time limited.
- Learning outcomes
- L04
Task description
Practical statistical exam which will pose questions similar to those seen during the prac classes. You are allowed access to web-based search engines and your notes during this exam. You will need your laptop and a statistical analysis program to sit this exam.
Exam details
| Planning time | no planning time minutes |
|---|---|
| Duration | 90 minutes |
| Calculator options | Any calculator permitted |
| Open/closed book | Open book examination - any written or printed material is permitted; material may be annotated |
| Materials | Laptop and internet |
| 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
- Hurdle
- Online
- Mode
- Written
- Category
- Examination, Quiz
- Weight
- Pass/Fail
- Due date
8/10/2025
Held online during timetabled lecture.
- Other conditions
- Time limited.
- 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. You are allowed access to web-based search engines and your notes during this exam.
Hurdle requirements
Students must complete this assessment item to pass this course overall, i.e. even if their final percentage mark is 50% or higher, the individual assessment item must have been completed.Exam details
| Planning time | no planning time minutes |
|---|---|
| Duration | 60 minutes |
| Calculator options | Any calculator permitted |
| Open/closed book | Open book examination - any written or printed material is permitted; material may be annotated |
| Materials | Laptop and internet |
| 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.
Research Proposal
- Mode
- Written
- Category
- Paper/ Report/ Annotation
- Weight
- 40%
- Due date
31/10/2025 2:00 pm
- Learning outcomes
- L01, L02
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.
Submission guidelines
To be submitted via the relevant Turnitin submission portal on the course Blackboard site.
When submitting an assignment, remember to include a cover sheet, for example -ﾠ
- Assessment Task: Assignment
- Course Title: Research Skills
- Course Code: HMST3846
- Student Name: Guy Wallis
- Student Number: 4xxxxxxxx
- Prac Group: P01
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.
- Include a screenshot of the error message.
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: Fails to satisfy most or all of the basic requirements of the course. |
| 2 (Fail) | 25 - 44 |
Minimal evidence of achievement of course learning outcomes. Course grade description: Fails to satisfy some of the basic requirements of the course. Clear deficiencies in performance, but evidence that some basic requirements have been met. |
| 3 (Marginal Fail) | 45 - 49 |
Demonstrated evidence of developing achievement of course learning outcomes Course grade description: Fails to satisfy all basic requirement for pass but is close to satisfactory overall and has compensating strengths in some aspects. |
| 4 (Pass) | 50 - 64 |
Demonstrated evidence of functional achievement of course learning outcomes. Course grade description: Satisfies all of the basic learning requirements for the course, such as knowledge of fundamental concepts and performance of basic skills; demonstrates sufficient quality of performance to be considered satisfactory or adequate or competent or capable in the course. |
| 5 (Credit) | 65 - 74 |
Demonstrated evidence of proficient achievement of course learning outcomes. Course grade description: Demonstrates ability to use and apply fundamental concepts and skills of the course, going beyond mere replication of content knowledge or skill to show understanding of key ideas, awareness of their relevance, some use of analytical skills, and some originality or insight. |
| 6 (Distinction) | 75 - 84 |
Demonstrated evidence of advanced achievement of course learning outcomes. Course grade description: Demonstrates awareness and understanding of deeper and subtler aspects of the course, such as ability to identify and debate critical issues or problems, ability to solve non-routine problems, ability to adapt and apply ideas to new situations, and ability to invent and evaluate new ideas. |
| 7 (High Distinction) | 85 - 100 |
Demonstrated evidence of exceptional achievement of course learning outcomes. Course grade description: Demonstrates imagination, originality or flair, based on proficiency in all the learning objectives for the course; work is interesting, surprising, exciting, challenging or erudite. |
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.)
The MCQ Statistics Exam is a course hurdle. Students must complete this assessment item to pass this course overall, i.e. even if their final percentage mark is 50% or higher, the individual assessment item must have been completed.
Supplementary assessment
Supplementary assessment is available for this course.
Additional assessment information
Use of generative Artificial Intelligence (AI) or Machine Translation (MT)
Students are advised that the unconsidered use of AI and MT technologies to develop, edit, or appraise their work is discouraged. Discourse with generative AI often results in vague or misleading answers in the absence of careful prompt engineering, and such technologies should be used with caution. The tasks in the course have been designed to be challenging, authentic and complex. Whilst students may use AI and/or MT technologies, successful completion of assessments 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.
All interactions with generative AI technologies must be declared in detail, including: the student’s prompt, the generated response, a critical appraisal of the quality of the interaction, and a summary of the intellectual impact of the interaction on the student’s work (if any).
A failure to reference generative AI or MT use or adequately attribute to them the intellectual contributions made to the student’s work may constitute student misconduct under the Student Code of Conduct.
To pass assessment tasks, students will be required to demonstrate detailed comprehension of their written submission independent of AI and MT tools.
Specific guidance about the appropriate use of AI in each assessment will be provided by course staff.
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
Find the required and recommended resources for this course 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 online instructions to obtain a copy of the course statistics software.
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 (28 Jul - 03 Aug) |
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 (04 Aug - 10 Aug) |
Lecture |
What is it you want to know? Knowledge, truth & research; Paradigm debates and issues. Classifying research. Learning outcomes: L01, L03 |
Week 3 (11 Aug - 17 Aug) |
No student involvement (Breaks, information) |
EKKA No lecture or prac this week due to EKKA holiday. |
Week 4 (18 Aug - 24 Aug) |
Practical |
Qualitative Assignment Prac Practice conducting and interpreting interviews. Learning outcomes: L02, L03 |
Lecture |
Principles of Research Design and Analysis Developing the research problem. Choosing the appropriate methodology. Analysng the results. Learning outcomes: L01 |
|
Week 5 (25 Aug - 31 Aug) |
Practical |
Variance and descriptive statistics Introduction to stats package Prism. Learning outcomes: L04 |
Lecture |
Why and when would you need to use statistics? Becoming acquainted with statistical concepts - sampling, measures of central tendency and variability. Learning outcomes: L02 |
|
Week 6 (01 Sep - 07 Sep) |
Practical |
Categorical Data, Reliability, Sample Size Learning outcomes: L02, L04 |
Lecture |
Methodological Considerations Validity and Reliability; Sample Size Calculations. Categorical data, rates and risk. Learning outcomes: L01, L04 |
|
Week 7 (08 Sep - 14 Sep) |
Practical |
Correlation and regression Learning outcomes: L01, L04 |
Lecture |
Relationships among variables Correlation and causation, partial correlation, and regression. Learning outcomes: L01, L04 |
|
Week 8 (15 Sep - 21 Sep) |
Practical |
Parametric tests (t tests and ANOVA) Learning outcomes: L01, L04 |
Lecture |
Differences between and among groups Independent and dependent t tests (one-tailed vs. two-tailed tests), and ANOVA. Learning outcomes: L01, L04 |
|
Week 9 (22 Sep - 28 Sep) |
Practical |
Multivariate analysis, non-parametric approaches Learning outcomes: L01, L04 |
Lecture |
Multivariate analysis and non-parametric analysis ANOVA for more than one variable. Alternative, non-parametric tests Learning outcomes: L01, L04 |
|
Mid Sem break (29 Sep - 05 Oct) |
No student involvement (Breaks, information) |
Mid-Semester Break No Practicals |
Week 10 (06 Oct - 12 Oct) |
Practical |
Statistical Analysis Exam Complete in allocated practical Learning outcomes: L01, L04 |
Week 11 (13 Oct - 19 Oct) |
Practical |
Ethics prac Discover the role of ethics in shaping how we conduct research with human participants. Consider issues relevant to your upcoming Research Proposal. Learning outcomes: L01 |
Lecture |
Reporting research (the good and the bad) Common mistakes in conducting statistical analysis. Scientific misconduct. Tackling the "Replication crisis". Learning outcomes: L01, 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 for 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: