PSYC5310: Semester 2, 2026

Units PSYC5310 Semester 2 2026 [Normal day]

Unit outline_

PSYC5310: Applied Advanced Psychology Research Methods

Semester 2, 2026 [Normal day] - Camperdown/Darlington, Sydney

Overview

This unit of study expands upon students' knowledge of the general linear model and its applications in the analysis of data from psychological research. One half of the unit introduces students to contrast analysis and interaction analyses as an extension of ANOVA, which allows for more focused analysis of data where group comparisons are the primary interest. Another half focuses on multiple regression and its extensions, which are used when the primary interest is to predict or explain a particular variable based on a set of other variables.

Unit details and rules

Academic unit	Psychology Academic Operations
Credit points	6
Prerequisites ?	PSYC5212
Corequisites ?	None
Prohibitions ?	None
Assumed knowledge ?	None
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Alissa Beath, alissa.beath@sydney.edu.au

Assessment

The census date for this unit availability is 31 August 2026

Details
Criteria

Type	Description	Weight	Due	Length	Use of AI
Written exam	Final Exam See the 'Assessment summary' below and Canvas site for details.	40%	Formal exam period	2 hours	AI prohibited
Outcomes assessed:
Data analysis	Assignment See the 'Assessment summary' below and Canvas site for details.	15%	Mid-semester break Due date: 02 Oct 2026 at 23:59 Closing date: 23 Oct 2026	See Canvas for details.	AI allowed
Outcomes assessed:
Practical skill	Tutorial Exercises See the 'Assessment summary' below and Canvas site for details.	20%	Multiple weeks	2 hours per week within tutorial classes	AI allowed
Outcomes assessed:
Interactive oral	Interactive Oral See the 'Assessment summary' below and Canvas site for details.	25%	Week 10	15 minutes	AI prohibited
Outcomes assessed:
= hurdle task ?

Assessment summary

Tutorial Exercises: Across each week's tutorial class, students will complete practical exercises using statistical software (R), applying and critically analysing research methods techniques and practices. Students will receive 2% for each weekly submission (completing 10 weeks' exercises for a maximim mark of 20%). See Canvas for details.

Assignment: Students will complete a written assignment requiring data analysis and interpretation. See Canvas for details.

Interactive Oral: Building off the written assignment, students will apply, extend, and critically analyse research methods to a novel, real-world context. See Canvas for details.

Final Exam: The entire unit, including lectures, tutorials, and associated tasks, will be assessed in a two-hour closed book short-answer exam held after the teaching period ends. Students who are approved Special Consideration to miss the Final Exam will sit a Replacement Exam, which will consist of short-answer questions. The Final Exam is a compulsory assessment, so any student who does not attempt it will receive an Absent Fail (AF) grade; it is also a hurdle assessment, so students will need to meet the required standard in order to pass the unit. See Canvas for details.

Assessment criteria

The University awards common result grades set out in the Coursework Policy 2021 (Schedule 1).

For more information see guide to grades.

Use of generative artificial intelligence (AI)

You can use generative AI tools for open assessments. Restrictions on AI use apply to secure, supervised assessments used to confirm if students have met specific learning outcomes.

Refer to the assessment table above to see if AI is allowed, for assessments in this unit and check Canvas for full instructions on assessment tasks and AI use.

If you use AI, you must always acknowledge it. Misusing AI may lead to a breach of the Academic Integrity Policy.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

Deduction of 5% of the maximum mark for each calendar day after the due date.
After ten calendar days late, a mark of zero will be awarded.

Academic integrity

The University expects students to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

Our website provides information on academic integrity and the resources available to all students. This includes advice on how to avoid common breaches of academic integrity. Ensure that you have completed the Academic Honesty Education Module (AHEM) which is mandatory for all commencing coursework students

Penalties for serious breaches can significantly impact your studies and your career after graduation. It is important that you speak with your unit coordinator if you need help with completing assessments.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

Learning support

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

Support for students

The Support for Students Policy reflects the University’s commitment to supporting students in their academic journey and making the University safe for students. It is important that you read and understand this policy so that you are familiar with the range of support services available to you and understand how to engage with them.

The University uses email as its primary source of communication with students who need support under the Support for Students Policy. Make sure you check your University email regularly and respond to any communications received from the University.

Learning resources and detailed information about weekly assessment and learning activities can be accessed via Canvas. It is essential that you visit your unit of study Canvas site to ensure you are up to date with all of your tasks.

If you are having difficulties completing your studies, or are feeling unsure about your progress, we are here to help. You can access the support services offered by the University at any time:

Support and Services (including health and wellbeing services, financial support and learning support)
Course planning and administration
Meet with an Academic Adviser

Weekly schedule

WK	Topic	Learning activity
Week 01	One-way ANOVA; Type I error correction	Lecture (2 hr)
Week 02	Two-way ANOVA; Two-way contrast analysis	Lecture (2 hr)
Week 02	Introduction and one-way ANOVA	Tutorial (2 hr)
Week 03	Simple main effects; Non-parametric ANOVA	Lecture (2 hr)
Week 03	Two-way ANOVA	Tutorial (2 hr)
Week 04	One-way & two-way repeated measures ANOVA	Lecture (2 hr)
Week 04	Two-way ANOVA contrasts	Tutorial (2 hr)
Week 05	Mixed Designs I & II	Lecture (2 hr)
Week 05	Simple main effects and non-parametric ANOVA	Tutorial (2 hr)
Week 06	Psychological measurement; summary of ANOVA	Lecture (2 hr)
Week 06	Repeated measures and mixed designs	Tutorial (2 hr)
Week 07	Simple linear regression; multiple regression I	Lecture (2 hr)
Week 07	Simple linear regression	Tutorial (2 hr)
Week 08	Multiple regression II & III	Lecture (2 hr)
Week 08	Multiple regression	Tutorial (2 hr)
Week 09	Multiple regression IV & V	Lecture (2 hr)
Week 09	Multiple regression	Tutorial (2 hr)
Week 10	Three types of regression, categorical variables in regression	Lecture (2 hr)
Week 11	Interaction with continuous variables I & II	Lecture (2 hr)
Week 11	Multiple regression	Tutorial (2 hr)
Week 12	Interaction with continuous variables III; Summary of MR	Lecture (2 hr)
Week 12	Interactions in multiple regression	Tutorial (2 hr)
Week 13	Overview and revision	Lecture (2 hr)
Week 13	Overview and revision	Tutorial (2 hr)

Attendance and class requirements

Each week, there will be 1 x 2-hour lecture (Tuesday 10am-12pm) for students to attend in person or watch online (via Canvas); and 1 x 2-hour tutorial class to attend in person (starting in Week 2, with no tutorials in Week 10).

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Required readings

Section 1: ANOVA (Weeks 1 – 6)

No set textbook; for students who wish to have a textbook, there are many ANOVA textbooks, such as Howell, D. C. Statistical Methods for Psychology. Belmont, CA: Wadsworth, Cengage Learning.

Section 2: MR (Weeks 7 – 12)

Set textbook: Keith, Z. T. (2006-2019). Multiple Regression and Beyond. Pearson New International Edition. USA: Pearson Education, Inc.

Learning outcomes

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University's graduate qualities and are assessed as part of the curriculum.

Outcomes
Graduate qualities

At the completion of this unit, you should be able to:

LO1. Analyse and apply advanced research methods to design and evaluate psychological studies.
LO2. Conduct, interpret, and critically evaluate statistical analyses relevant to complex psychological research questions, using statistical software effectively.
LO3. Critically assess the strengths and limitations of research methods and findings, including consideration of ethical implications, cultural contexts, and theoretical and methodological assumptions that shape data interpretation.
LO4. Use research methods to investigate and pose solutions to real-world psychological problems, demonstrating the capacity to make informed decisions in the face of ambiguity and complexity.
LO5. Communicate research processes and findings clearly and accurately, using discipline-specific conventions and tailored to different audiences and purposes.

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1	Depth of disciplinary expertise Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.
GQ2	Critical thinking and problem solving Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.
GQ3	Oral and written communication Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.
GQ4	Information and digital literacy Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.
GQ5	Inventiveness Generating novel ideas and solutions.
GQ6	Cultural competence Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.
GQ7	Interdisciplinary effectiveness Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.
GQ8	Integrated professional, ethical, and personal identity An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.
GQ9	Influence Engaging others in a process, idea or vision.

Outcome map

Learning outcomes	Graduate qualities
Learning outcomes

Responding to student feedback

This section outlines changes made to this unit following staff and student reviews.

This is a new unit running for the first time in 2026.

Disclaimer

Important: the University of Sydney regularly reviews units of study and reserves the right to change the units of study available annually. To stay up to date on available study options, including unit of study details and availability, refer to the relevant handbook.

To help you understand common terms that we use at the University, we offer an online glossary.

Current students

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links