| Unit convenor and teaching staff |
Unit convenor and teaching staff
Convenor and lecturer
Erik Reichle
Co-convenor
Kevin Brooks
|
|---|---|
| Credit points |
Credit points
10
|
| Prerequisites |
Prerequisites
Admission to BPsych or BPsych (Hons) AND 130cp at the 1000 level or above including PSYU2248 or PSYX2248
|
| Corequisites |
Corequisites
|
| Co-badged status |
Co-badged status
|
| Unit description |
Unit description
This unit builds on and unifies statistical and design topics introduced in previous units, particularly PSYU2248 Design and Statistics II. Topics include: repeated measures and mixed design ANOVA, multiple regression (linear, curvilinear, and logistic); analysis of variance and covariance; and model reduction procedures. The unit also illustrates the links between these different methods through placing them in the context of the Generalized Linear Model; in so doing the unit enhances your understanding of statistical methods and their relationship with research design. Practical classes utilise the Stata statistical package. |
Information about important academic dates including deadlines for withdrawing from units are available at https://www.mq.edu.au/study/calendar-of-dates
On successful completion of this unit, you will be able to:
General assessment Information
Grade descriptors and other information concerning grading are contained in the Macquarie University Assessment Policy.
All final grades are determined by a grading committee, in accordance with the Macquarie University Assessment Policy, and are not the sole responsibility of the Unit Convenor.
Students will be awarded a final grade and a mark which must correspond to the grade descriptors specified in the Assessment Procedure (clause 128 and 129).
To pass this unit, you must demonstrate sufficient evidence of achievement of the learning outcomes, meet any ungraded requirements, and achieve a final mark of 50 or better.
Further details for each assessment task will be available on iLearn.
Unless a Special Consideration request has been submitted and approved, a 5% penalty (OF THE TOTAL POSSIBLE MARK) will be applied each day an assessment is not submitted, up until the 7th day (including weekends). After the 7th day, a grade of ‘0’ will be awarded even if the assessment is submitted. Submission time for all written assessments is set at 11.55pm. A 1-hour grace period is provided to students who experience a technical concern.
For example:
|
Number of days (hours) late |
Total Possible Marks |
Deduction |
Raw mark |
Final mark |
|
1 day (1-24 hours) |
100 |
5 |
75 |
70 |
|
2 days (24-48 hours) |
100 |
10 |
75 |
65 |
|
3 days (48-72 hours) |
100 |
15 |
75 |
60 |
|
7 days (144-168 hours) |
100 |
35 |
75 |
40 |
|
>7 days (>168 hours) |
100 |
- |
75 |
0 |
For any late submissions of time-sensitive tasks, such as scheduled tests/exams, performance assessments/presentations, and/or scheduled practical assessments/labs, students need to submit an application for Special Consideration.
| Name | Weighting | Hurdle | Due | Groupwork/Individual | Short Extension | AI Approach |
|---|---|---|---|---|---|---|
| Data Analysis Task | 20% | No | Week 7, in tutorial class | Individual | No | Open |
| Practical Project | 40% | No | Week 8; details see iLearn | Individual | No | Open |
| Final Examination | 40% | No | University final exam period | Individual | No | Observed |
Assessment Type 1: Problem-based task
Indicative Time on Task 2: 20 hours
Due: Week 7, in tutorial class
Weighting: 20%
Groupwork/Individual: Individual
Short extension 3: No
AI Approach: Open
You will analyse and report on data to demonstrate the analyses presented in the unit.
Assessment Type 1: Practice-based task
Indicative Time on Task 2: 35 hours
Due: Week 8; details see iLearn
Weighting: 40%
Groupwork/Individual: Individual
Short extension 3: No
AI Approach: Open
Practical project requiring data analysis and a written report to address a research question within the context of psychology research
Assessment Type 1: Examination
Indicative Time on Task 2: 38 hours
Due: University final exam period
Weighting: 40%
Groupwork/Individual: Individual
Short extension 3: No
AI Approach: Observed
Final examination held within the University’s formal exam period, in accordance with relevant requirements.
1 If you need help with your assignment, please contact:
2 Indicative time-on-task is an estimate of the time required for completion of the assessment task and is subject to individual variation.
3 An automatic short extension is available for some assessments. Apply through the Service Connect Portal.
As a student enrolled in this unit, you will engage in a range of face-to-face and online learning activities, including readings, lectures, and tutorials, etc. Details can be found on the iLearn site for this unit.
Active participation in the learning activities throughout the unit will require students to have access to a tablet, laptop, or similar device. Students who do not own their own laptop computer may borrow one from the university library.
You are expected to have had prior experience in the use of Stata before coming into PSYU3349, and be able to read raw data files, access pre-existing data files, and retrieve Stata data files. You are also expected to have some knowledge of Stata syntax. You can directly download Stata to your own computer from MQ's website https://students.mq.edu.au/support/technology/software/stata, following the instructions closely. If you experience technical issues, contact IT Help https://students.mq.edu.au/support/technology/service-desk
Competent use of Stata is required heading into PSYU3349. If you need a refresher on Stata, then this playlist offers a good place to start: https://www.youtube.com/playlist?list=PLN5IskQdgXWnnIVeA_Y0OBGmnw21fvcmU
|
Week |
Reading |
Topic |
|
1 |
Agresti 9 (revision) & 11 |
Multiple regression |
|
2 |
Agresti 12.1-12.4 |
ANOVA by regression I |
|
3 |
Agresti 12.1-12.4 |
ANVOA by regression II |
|
4 |
Agresti 13.1-13.2 |
ANCOVA |
|
5 |
Agresti 14.5 |
Curvilinear relationships |
|
6 |
Agresti 5.5 & 14.2 |
Badly behaved data |
|
7 |
Agresti 14.1 & notes |
Model reduction |
|
- |
N/A |
N/A |
|
8 |
Agresti 8.1-8.2 & 15.1 |
Categorical data & logistic regression I |
|
9 |
Agresti 15.1-15.3 |
Categorical data & logistic regression II |
|
10 |
Howell 7.4 |
Paired t-test & repeated measures |
|
11 |
Howell 14.1-14.5 |
Repeated measures I |
|
12 |
Howell 14.7 |
Repeated measures II & mixed designs |
|
13 |
n/a |
Review |
*Please note that lecture content and schedule are subject to change. Please see iLearn for tutorial schedule.
Macquarie University policies and procedures are accessible from Policy Central (https://policies.mq.edu.au). Students should be aware of the following policies in particular with regard to Learning and Teaching:
Students seeking more policy resources can visit Student Policies (https://students.mq.edu.au/support/study/policies). It is your one-stop-shop for the key policies you need to know about throughout your undergraduate student journey.
To find other policies relating to Teaching and Learning, visit Policy Central (https://policies.mq.edu.au) and use the search tool.
Macquarie University students have a responsibility to be familiar with the Student Code of Conduct: https://students.mq.edu.au/admin/other-resources/student-conduct
Results published on platform other than eStudent, (eg. iLearn, Coursera etc.) or released directly by your Unit Convenor, are not confirmed as they are subject to final approval by the University. Once approved, final results will be sent to your student email address and will be made available in eStudent. For more information visit connect.mq.edu.au or if you are a Global MBA student contact globalmba.support@mq.edu.au
At Macquarie, we believe academic integrity – honesty, respect, trust, responsibility, fairness and courage – is at the core of learning, teaching and research. We recognise that meeting the expectations required to complete your assessments can be challenging. So, we offer you a range of resources and services to help you reach your potential, including free online writing and maths support, academic skills development and wellbeing consultations.
Macquarie University provides a range of support services for students. For details, visit http://students.mq.edu.au/support/
Academic Success provides resources to develop your English language proficiency, academic writing, and communication skills.
The Library provides online and face to face support to help you find and use relevant information resources.
Macquarie University offers a range of Student Support Services including:
Got a question? Ask us via the Service Connect Portal, or contact Service Connect.
For help with University computer systems and technology, visit http://www.mq.edu.au/about_us/offices_and_units/information_technology/help/.
When using the University's IT, you must adhere to the Acceptable Use of IT Resources Policy. The policy applies to all who connect to the MQ network including students.
Macquarie University recognises that artificial intelligence (AI), especially generative AI, is rapidly reshaping education and the modern workplace. As AI becomes increasingly accessible, the University and your teaching staff are committed to preparing you to use these tools effectively, ethically, and with strong professional judgment. Rather than restricting technology, the emphasis is on helping you understand when and how AI can be used to enhance productivity, support learning, and reflect real-world professional practice. Across your degree, we will support you to develop the critical thinking, adaptability, and values-based decision-making skills required to navigate evolving AI tools responsibly, including acknowledging their use appropriately. You should always appropriately acknowledge when you have used AI tools within assessment tasks, including which AI tools you have used and how you have used them.
To provide clarity, Macquarie University uses a simple, two-tiered approach to AI in assessment:
Across both categories, the goal is to ensure you build foundational knowledge, exercise sound judgment, and engage with AI in ways that uphold ethical, cultural, and university values.
Social inclusion at Macquarie University is about giving everyone who has the potential to benefit from higher education the opportunity to study at university, participate in campus life and flourish in their chosen field. The University has made significant moves to promote an equitable, diverse and exciting campus community for the benefit of staff and students. It is your responsibility to contribute towards the development of an inclusive culture and practice in the areas of learning and teaching, research, and service orientation and delivery. As a member of the Macquarie University community, you must not discriminate against or harass others based on their sex, gender, race, marital status, carers' responsibilities, disability, sexual orientation, age, political conviction or religious belief. All staff and students are expected to display appropriate behaviour that is conducive to a healthy learning environment for everyone.
In the Faculty of Medicine, Health and Human Sciences, professionalism is a key capability embedded in all our courses.
As part of developing professionalism, students are expected to attend all small group interactive sessions including clinical, practical, laboratory, work-integrated learning (e.g., PACE placements), and team-based learning activities. Some learning activities are recorded (e.g., face-to-face lectures), however you are encouraged to avoid relying upon such material as they do not recreate the whole learning experience and technical issues can and do occur. As an adult learner, we respect your decision to choose how you engage with your learning, but we would remind you that the learning opportunities we create for you have been done so to enable your success, and that by not engaging you may impact your ability to successfully complete this unit. We equally expect that you show respect for the academic staff who have worked hard to develop meaningful activities and prioritise your learning by communicating with them in advance if you are unable to attend a small group interactive session.
Another dimension of professionalism is having respect for your peers. It is the right of every student to learn in an environment that is free of disruption and distraction. Please arrive to all learning activities on time, and if you are unavoidably detained, please join activity as quietly as possible to minimise disruption. Phones and other electronic devices that produce noise and other distractions must be turned off prior to entering class. Where your own device (e.g., laptop) is being used for class-related activities, you are asked to close down all other applications to avoid distraction to you and others. Please treat your fellow students with the utmost respect. If you are uncomfortable participating in any specific activity, please let the relevant academic know.
Unit information based on version 2026.04 of the Handbook